{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "00_Keras_RNN_predictions_solution.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/tensorflow-without-a-phd/blob/master/tensorflow-rnn-tutorial/00_Keras_RNN_predictions_solution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "_wxmLPc2YysH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# An RNN for short-term predictions\n",
        "This model will try to predict the next value in a short sequence based on historical data. This can be used for example to forecast demand based on a couple of weeks of sales data.\n",
        "\n",
        "This is the solution notebook. The corresponding work notebook is here: [00_Keras_RNN_predictions_playground.ipynb](https://colab.research.google.com/github/GoogleCloudPlatform/tensorflow-without-a-phd/blob/master/tensorflow-rnn-tutorial/00_Keras_RNN_predictions_playground.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "3uk3AMTcYysJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import tensorflow as tf\n",
        "tf.enable_eager_execution()\n",
        "print(\"Tensorflow version: \" + tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Sk3oZyKsZLRf",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#@title Display utilities [RUN ME]\n",
        "\n",
        "from enum import IntEnum\n",
        "import numpy as np\n",
        "\n",
        "class Waveforms(IntEnum):\n",
        "    SINE1 = 0\n",
        "    SINE2 = 1\n",
        "    SINE3 = 2\n",
        "    SINE4 = 3\n",
        "\n",
        "def create_time_series(waveform, datalen):\n",
        "    # Generates a sequence of length datalen\n",
        "    # There are three available waveforms in the Waveforms enum\n",
        "    # good waveforms\n",
        "    frequencies = [(0.2, 0.15), (0.35, 0.3), (0.6, 0.55), (0.4, 0.25)]\n",
        "    freq1, freq2 = frequencies[waveform]\n",
        "    noise = [np.random.random()*0.2 for i in range(datalen)]\n",
        "    x1 = np.sin(np.arange(0,datalen) * freq1)  + noise\n",
        "    x2 = np.sin(np.arange(0,datalen) * freq2)  + noise\n",
        "    x = x1 + x2\n",
        "    return x.astype(np.float32)\n",
        "\n",
        "from matplotlib import transforms as plttrans\n",
        "\n",
        "plt.rcParams['figure.figsize']=(16.8,6.0)\n",
        "plt.rcParams['axes.grid']=True\n",
        "plt.rcParams['axes.linewidth']=0\n",
        "plt.rcParams['grid.color']='#DDDDDD'\n",
        "plt.rcParams['axes.facecolor']='white'\n",
        "plt.rcParams['xtick.major.size']=0\n",
        "plt.rcParams['ytick.major.size']=0\n",
        "\n",
        "def picture_this_1(data, datalen):\n",
        "    plt.subplot(211)\n",
        "    plt.plot(data[datalen-512:datalen+512])\n",
        "    plt.axvspan(0, 512, color='black', alpha=0.06)\n",
        "    plt.axvspan(512, 1024, color='grey', alpha=0.04)\n",
        "    plt.subplot(212)\n",
        "    plt.plot(data[3*datalen-512:3*datalen+512])\n",
        "    plt.axvspan(0, 512, color='grey', alpha=0.04)\n",
        "    plt.axvspan(512, 1024, color='black', alpha=0.06)\n",
        "    plt.show()\n",
        "    \n",
        "def picture_this_2(data, batchsize, seqlen):\n",
        "    samples = np.reshape(data, [-1, batchsize, seqlen])\n",
        "    rndsample = samples[np.random.choice(samples.shape[0], 8, replace=False)]\n",
        "    print(\"Tensor shape of a batch of training sequences: \" + str(rndsample[0].shape))\n",
        "    print(\"Random excerpt:\")\n",
        "    subplot = 241\n",
        "    for i in range(8):\n",
        "        plt.subplot(subplot)\n",
        "        plt.plot(rndsample[i, 0]) # first sequence in random batch\n",
        "        subplot += 1\n",
        "    plt.show()\n",
        "    \n",
        "def picture_this_3(predictions, evaldata, evallabels, seqlen):\n",
        "    subplot = 241\n",
        "    colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
        "    for i in range(8):\n",
        "        plt.subplot(subplot)\n",
        "        #k = int(np.random.rand() * evaldata.shape[0])\n",
        "        l0, = plt.plot(evaldata[i, 1:], label=\"data\")\n",
        "        plt.plot([seqlen-2, seqlen-1], evallabels[i, -2:])\n",
        "        l1, = plt.plot([seqlen-1], [predictions[i]], \"o\", color=\"red\", label='Predicted')\n",
        "        l2, = plt.plot([seqlen-1], [evallabels[i][-1]], \"o\", color=colors[1], label='Ground Truth')\n",
        "        if i==0:\n",
        "            plt.legend(handles=[l0, l1, l2])\n",
        "        subplot += 1\n",
        "    plt.show()\n",
        "    \n",
        "def picture_this_hist(rmse1, rmse2, rmse3, rmse):\n",
        "  colors = ['#4285f4', '#34a853', '#fbbc05', '#ea4334']\n",
        "  plt.figure(figsize=(5,4))\n",
        "  plt.xticks(rotation='40')\n",
        "  plt.title('RMSE: your model vs. simplistic approaches')\n",
        "  plt.bar(['RND', 'LAST', 'LAST2', 'Yours'], [rmse1, rmse2, rmse3, rmse], color=colors)\n",
        "  plt.show()\n",
        "\n",
        "def picture_this_hist_all(rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8):\n",
        "  colors = ['#4285f4', '#34a853', '#fbbc05', '#ea4334', '#4285f4', '#34a853', '#fbbc05', '#ea4334']\n",
        "  plt.figure(figsize=(7,4))\n",
        "  plt.xticks(rotation='40')\n",
        "  plt.ylim(0, 0.35)\n",
        "  plt.title('RMSE: all models')\n",
        "  plt.bar(['RND', 'LAST', 'LAST2', 'LINEAR', 'DNN', 'CNN', 'RNN', 'RNN_N'],\n",
        "          [rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8], color=colors)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RJ-1YaKQYysO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Generate fake dataset"
      ]
    },
    {
      "metadata": {
        "id": "pAhYNwMUYysP",
        "colab_type": "code",
        "outputId": "6338b0e2-1d3a-48f0-b655-ad83aa34af9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        }
      },
      "cell_type": "code",
      "source": [
        "DATA_SEQ_LEN = 1024*128\n",
        "data = np.concatenate([create_time_series(waveform, DATA_SEQ_LEN) for waveform in Waveforms]) # 4 different wave forms\n",
        "picture_this_1(data, DATA_SEQ_LEN)\n",
        "DATA_LEN = DATA_SEQ_LEN * 4 # since we concatenated 4 sequences"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QKBZ5iMXy2po2ko0jFOQYo3w7g8wWG0g3uu2FggFkUhHGKFvg3Q+9iP/v4bNY361geVOr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5xNol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p11Gc4P/5gyfxY+98FM/Mrxufy1oEVajXAL1f65R2an/iQnaG2J2O+kvUa1crR5EtF/Pa3/3K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\nbMf19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KiQsRGQiXIEbM+xaKbfnm3BRjuRIBEZH67gtquSXYQlwqAsFk1yo2CDRr2gCnua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\ngsH/D8jBTmHSE6C7AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Yt3TIQ6EYysS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Hyperparameters"
      ]
    },
    {
      "metadata": {
        "id": "FNw-tcLPYysS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "RNN_CELLSIZE = 32   # size of the RNN cells\n",
        "SEQLEN = 16         # unrolled sequence length\n",
        "BATCHSIZE = 32      # mini-batch size\n",
        "LAST_N = SEQLEN//2  # loss computed on last N element of sequence in advanced RNN model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bzLvRzgjYysU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Visualize training sequences\n",
        "This is what the neural network will see during training."
      ]
    },
    {
      "metadata": {
        "id": "IxTQ2-BRYysV",
        "colab_type": "code",
        "outputId": "4a5fdb1c-6898-4e46-c8dd-52ee5772c440",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "cell_type": "code",
      "source": [
        "picture_this_2(data, BATCHSIZE, SEQLEN) # execute multiple times to see different sample sequences"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor shape of a batch of training sequences: (32, 16)\n",
            "Random excerpt:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V8enmQqyVcu6YPWrAH3+ErzMjfJw4cKyGtg7NoFdNCIIgnG9fbiU5xXXEjvEm\nJlz0ariSSZE+zE8I4ru9J/hgYy6P3jZe6kh9cqlj8d4m0sZOp9Pzz4+yaW3X8Oht44yqQmCwLUgZ\nzYFj1Xy6+ThjQ4dmIi1cm36dlmxtbeWBBx6gu7sbgKysLEaNGkVCQsK5lem8vDy8vLxEWXc/Faub\nWL+lEE83Ox68OUrqOBbN3taKRXPD6ezWse4HsfekqftpXyl1TR1cPzUID5fB2Ys9MdofrU5PRk7F\noDy+IAjCpXRrdHzwbR4KuYwHbors/QcE7rsxkhE+TnyffpL9uZVSx+nV5Y7FTd0nm4+Tp6pn6jhf\nro8fKXWcIRU2wp3o0Z7kFNeRp6qXOo5wBb2uSOfm5vLKK69QXl6OUqkkLS2NpKQk/P39SUlJITEx\nkdTUVGxsbIiIiGDu3LnIZDIiIyNZsGABMpmMZcuWDcVYzJZGq+fNLw6i1xt48s5o7G3FipbUkuMC\n2bhbxZasU9w4PdiizpSaky6Njs+2FmJjreD2pME78EiM9mPtD8fYdaiclMkjBu15BEEQzrdhVwlV\n9e3cMiMEfy8nqeOYBBsrBc8uiuXpf+3kzfWHeSvAddBOsvbVtRyLm7IjRbV8tqUQL3d7/u/OaIvs\nXp2aEsahwlrWbz7OX389Veo4wmX0OpGOiopi7dq1l719yZIlLFmy5KLvP/PMM/1LJpzzfWY16upW\nbkgIYvzoK5fIC0NDoZBz/42RvLhyH6u/zRNvcibqx4yTNLR0cdusUFydBq+LrY+HA2Ej3MgprqWx\npXPQnkcQBOGshpZOPt9aiLOD9YBt6WcpRvg688CNkbzz9VHe+OQgf314KnIJt7y81mNxU9TU2sVr\nH2Ujl8n43aIYi70cKjLYg3GhwzhUWMvx0gbCRrhLHUm4BNFxwsjln6hn+6E6fIc5sGR+hNRxhPNM\nDPNiwmhPDhXWcrBgKNqcCQOps0vLF1uLsLNR8qtZg18Glxjth94Au4+UD/pzCYIgrPk+n44uHYuv\nH2Oxk5H+mJcQRFyEN0eK6vh6R7HUcSyCXm/gjU8O0tjaxT3zxlj85DE1pWcXkU83F0qcRLgcMZE2\ncqs25gLw1IJosdWSkZHJZNx/YyQyGXzwbS46fe/d6QXj8d3eEzS1dXFTYjDODtaD/nzTx/shl8Gu\nQ2IiLQjC4Co81cjWLDVBw53F5STXSCaT8WRqNG5ONqz94RhF6kapI5m9r3YUc/B4DTHhXtwyI1Tq\nOJIbGzKMiCB3DhyrpljdJHUc4RLERNqI1TS0U3iqibAARyKCPKSOI1xC0HAXkuMCKa1qZUtmqdRx\nhD5q79Tw5fZiHOyshuzD2s3p/3SvAAAgAElEQVTZlnGhnhwvbaSuuXtInlMQBMtjMBhY+c1RAB66\nZSwKCUuSTZ2Low1P3zURnd7AP9Zl09GllTqS2So42cDaH47h7tzz31zKUnpjIZPJWHDmsoyvRFWE\nURITaSOWcaZb5PgQ0cjKmN09NxwbawXrfiwQH7Im4tvdKlrbu7l1RsiQljwmRvfsI70nV3ThtBQG\ng/FWqhQWFpKcnMy6deukjiIMoJ2HyikobSRh3PAh24PWnEWHeXHrzFAq607z3tdHpY5jltrau3l1\n3QEMBgNL747BxXHwepaYmgmjPQnwdmRfbiVt7eIkvLERE2kjlnG0EpkMooJEp01j5uFix20zQ2lq\n7eLL7UVSxxF60dah4eudJTjZW3Pj9KHdr3v6BD98POzZcURsaWHuOru0rFi9n3+sLzbKyXR7ezvL\nly8nPj5e6ijCAGpu6+L9DUexVsq59wbRV2WgLL5+DCH+LmzJOkXuiRap45gVg8HAvz87TG1jBwtS\nwhgXKprqnk8mk5EUG4hGq2f3YXFpmLERE2kj1djSSf6JeiKCPHC2F01CjN2tM0Nxd7bh6x0l1Dd3\nSB1HuIJvdhZzukPDbbNCh3wrOVsbJb+9KwaA1z85SHunZkifXxgare3d/OnddPblVuHpam2UW7dY\nW1uzcuVKvLy8pI4iDKD3vjlKc1s3i64fg4+Hg9RxzIaVUs6zi2Lx8bCnvUsndRyz8v3eE2QcrSQq\nxEN0l7+MWTH+yGWw9YBa6ijCL4juVUZqX14VBgNMHesrdRShD2xtlCyaO4Z/f3aYtT8c46kFE6WO\nJFxCy+luNu5S4epow/yEIEkyjAlyJ2WiJz9l1/Lu10d5+i7xt2JO6ps7eOG9DE5VtTIzxp+b4lyl\njnRJSqUSpbLvhwBqtRqNpvcTPyqVqj+xTJYxjPuoqoVdh8oZ6W3HWH/ZkGQyhnEPpecXhAC9jzs4\neGirnUyVqryZ9zfm4exgzTN3x4jr+S/Dw8WOCaO9OHi8hrKaVrEnvBERE2kjlZFTAcCUsb60NVZJ\nnEboi6S4QDbuVrHtgJqbpocQ7CeubTc2X20voqNLy8I54ZJ2wZ8b542qRsO2A2omRfiQMH64ZFmE\ngVNR28af38ugpqGdG6cH8+BNUZw8eULqWAMiICCg1/uoVCqLnEAYw7hb27v5cs02rJRyfrckngDv\nwT/QNoZxS8FSxz3QOru1vLo2C61Oz9N3TcTDxU7qSEYtKTaAg8dr2HZAzT3zxGUbxkKUdhuhtvZu\ncorrCA1wxcvNXuo4Qh8p5D3bYRkMPdthGeN1kZassbWTTXtP4O5sy/VTR0qaRaGQsXThRKytFLz9\nxWFxOYAZKClr4rn/7KGmoZ1Fc8N56OYo0XVWGDIrvzlKY2sXd10XNiSTaEHorw27SiivPc1NicHE\njvGWOo7RmzLWF3tbJdsPqMV2q0ZETKSNUGZ+FTq9QZR1m6DoMC9iwr04UlTHgWPVUscRzvPltmK6\nunXcOXsUNlYKqePg7+XE/TdG0tqu4c1PD6EXH4wmK7ekjj/8dy/Np7t49LZxpKaEGeV10YJ5ysqv\nYnt2GaEBrvxqpth7VzB+zW1dfLmtGCd7a+6eEy51HJNgY6Vg+gQ/6po7ySmqlTqOcIaYSBuh9Jye\nba+mjhPlnqbovhsjkctg9aY8dDq91HEEeq5b/SH9BMNc7bhuygip45wzb+pIYsK9OFRYy3d7zaME\n2NLsz61k2XsZdGt0PHt3LPOmSnPt/dXKzc1l8eLFfP3116xZs4bFixfT1NQkdSzhKrV1aPjP50dQ\nKmQ8lRqNQiEO6wTj99mWQjq6tCxIGT3kTT9NWVJszyU220TTMaMhrpE2Mh1dWg4dryHQxwk/T0ep\n4wjXYISPMymTR5C2r5Sf9pdyvYkcWJuzz7cW0a3VsyBlNFZK6Vejz5LJZDyZGs3j/9zO/zblMX7U\nMAJ9nKWOJfTR1qxT/Puzw1gp5fx5yWQmhptOB+yoqCjWrl0rdQyhnz7YmEtDSyeL5oYzwle8dwjG\nr6r+NN+nn8Db3V7yy6xMzZiR7vgOcyD9aCWPdmrESQgjIE5dGpnsgmq6tXriRVm3Sbt7Tjh2Ngo+\nSiugo0srdRyLVtPYTtq+Unw87JkdFyh1nIu4Odvy+B0T6Nbqee2jg2i0oorBFHyzs5h/fXoIexsl\nLz0y1aQm0YJ5OFhQw+bMUwT7uXBb0iip4whCn6z7oQCtzsDi68cY1YltUyCTyZgdG0C3RseeIxVS\nxxEQE2mjk3GmrDtBlHWbNDdnW25ODKW5rZttWaekjmPRPttSiFanZ0FKGEojLXuMH+tLyqRAVBXN\nfJxWIHUc4QoMBgNrvs9n1cY83J1t+fvj0wgf4S51LMHCtHdqeOvzwyjkMp5aEG20722CcL7isiZ2\nHiojxN+F6RP8pI5jkmbFiPJuY9Knd97CwkKSk5NZt27dRbft27ePO++8kwULFvD888+j1+vZv38/\nU6ZMYfHixSxevJjly5cPeHBz1K3RkXWsCh8Pe0aKEi2TNz8hCKVCzsbdKpNoJPXyyy+TmprKggUL\nyMnJueC2pKQkFi5ceO41XV1tGo3UqupPsyXzFH6eDsyc6C91nCt68OYofDzs+XJ7EbkldVLHES5B\npzfw9hdH+HxrEb7DHHj1/6YzQpTiCxJYvSmfuqYO7pg9mqDhYqtFS3ClY/H09HRuv/12UlNTefvt\ntyVI1zcfbsoH4N75EWJXg2vk5W7PuNBh5Knqqaw7LXUci9frNdLt7e0sX76c+Pj4S97+wgsvsGbN\nGnx8fHjiiSfYvXs3tra2TJo0iX//+98DHticHSmqpaNLx9z44aLjqxlwdbJh5kR/tmSdIrugmrgI\nH6kjXVZmZialpaWsX7+ekpIS/vCHP7B+/foL7rNy5UocHBwkSnhtPt18HJ3ewF3XhRt9Ex57WyuW\nLozhuf/s5o1PDvLvpbNwsBPXPxkDg8FAcVkTn/5USGZ+FcHDXXjx4Sm4OdlKHU2wQEeKavkx4yQj\nfZ25M3m01HGEIdDbsfhLL73EqlWr8Pb2ZtGiRcyZM4fQUOPq4H7weA2Hi2qJHu3JhNHiUpj+mB0X\nQE5xHdsOqLl7ruh6LqVejyytra1ZuXIlXl6X/qP/6quv8PHpmSC4u7vT2Ng4sAktyLlu3eL6aLNx\nU2IwABt3qSROcmUZGRkkJycDEBISQnNzM21tbRKn6p/KutNsP6Am0MfJZErIwke6c0fyaGoaO3j3\n65zef0AYVM1tXWzYVcITr+3gt//aRWZ+FVEhHrz8mwQxiRYk0dGl5d+fHUYu72lUaKU07hOEwsC4\n0rG4Wq3GxcUFX19f5HI5M2bMICMjQ4KUl6fXG/hwUz4yGdx7Q6TUcUxe/Njh2For2JatNomKR3PW\n64q0UqlEqbz83RwdezpL19TUsHfvXp588kkKCwspLi7mkUceobm5mccff5yEhIQrPo9arUaj0fQa\nWKUy7gnJtdLpDaTnlOPioESpa0SlunAbEnMdd2/MYdyj/Bw4XFTLnqx8hnv07eC7t3EHBwcPRLRz\n6urqiIz8+cPN3d2d2trac69vgGXLllFeXk5MTAxLly41+qqJ7dlq9Aa4bdYokyohW5ASxsGCGrZn\nlzEp0odp403jJIC50OkNHDpew5bMU+zPq0SrM6BUyJg6zpeUSSOIDvNCYUJ/T4J5+fC7fGoa2rlj\n9ihCA1yljiMMkSsdi9fW1uLu/nOfBnd3d9Tq3q+fHcrj7gPHG1FVNBM72hW66lGp6vv9mIPN2I8/\nxwU7kVnQxJa9Rwn1G5hdfox9zIOlL+O+3HH3gGx/VV9fzyOPPMKyZctwc3Nj5MiRPP7441x//fWo\n1WruuecefvrpJ6ytrS/7GAEBAb0+j0qlGvAJhLE4UlRLe5eO+bFBhIaEXHCbOY/7Ssxl3KnX2fHS\n6kwOnehmWlxEr/c3hnEbDBee4XziiSeYPn06Li4uPPbYY6SlpTF37twrPoaUJ8cMBgPbsk5ipZDh\n49hplB8OV8p0x3Qv/vFZM2+tP4S9rA1XR/Mo8TbG38NZtU1dZBY0sr+gkebTPZ32fdxtmDLGnbgw\nVxztlMBpSk9e/X7fQ31iTDBPR0vq+G7vCQK8HVmQEiZ1HMHEDdVxt0arY8UnJSgVch65Iw5vd/t+\nPd5QMIbjsN7concms2Av+eVarpve/6ymMObB0N9x93si3dbWxkMPPcRTTz3FtGnTAPD29mbevHkA\nBAYGMmzYMKqrq/v0orVU6Tk9bezFtlfmJzbCB18PB7Znq7ln3hhcHG2kjnQRLy8v6up+bnBVU1OD\np6fnua9vueWWc/9OTEyksLCw14m0lCfHTlQ0U92Yy9RxvkSEG9+2ML2NOxh4qMuW//dlDl+n1/OX\nh+NNalX9UozxQ7qzS0v60Qo2Z54it6RnhcTeVsnc+JGkTApkVIBrvysvjHHcgunp7Nby1vrDyGXw\nZGo01lZi2yChxy8/v6urqy97OaYUvk8/SU1DOzcnhpjEJNpURAZ74OVmR3pOBY/cOg5bmwFZGxWu\nUr8vrvn73//OkiVLSExMPPe9jRs3smrVKqCn5KS+vh5vb+/+PpXZ0usN7MutxMnemqhgD6njCANM\nIZdxw/QgNFo9P+47KXWcS0pISCAtLQ2AvLw8vLy8zpV1t7a28sADD9Dd3Q1AVlYWo0YZ3+T0fLsP\nlwOYzLXRlzI3fiSxY7w5XFTLpj3Gu5JrqorUjdz71zTe+OQQuSX1jA0Zxm8XTuTDZXN47PbxjA50\nM/rLFwTLsfaHY1TWn+aWGaGEie3WhPP4+/vT1tZGWVkZWq2W7du393o55VA53aFh/eZCHGyVojHe\nAJPLZcyKDaCjS0f60Uqp41isXk9f5Obm8sorr1BeXo5SqSQtLY2kpCT8/f2ZNm0a33zzDaWlpXzx\nxRcA3HDDDcyfP59nnnmGrVu3otFoePHFF69Y1m3pCk810tDSRcqkQKPvLCxcm+S4QD76sYDv957g\nVzNHGV2DmIkTJxIZGcmCBQuQyWQsW7aMr776CicnJ1JSUkhMTCQ1NRUbGxsiIiJ6XY2WksFgYM+R\nCmysFcSOMd0TeDKZjCdSJ/D4P7az5odjJIwfjoeLndSxzMbGXSpOd2q5dWYo18ePxHeYaXWkFyxH\n/ol6vt2tws/TgYWiQ69FutKxeEpKCi+++CJLly4FYN68eQQFBUmcuMeX24tobe/mnnljcHYQ84CB\nNjs2kPWbC9madYqkWFH1K4VeJ9JRUVGsXbv2srfn5uZe8vvvvPPOtaeyMGfPJImybvNlb2tFyqQR\nbNhVwt4j5cyMMb43vGeeeeaCr8PDfz5gW7JkCUuWLBnqSNekpLyZyrrTTJ/gh621aZc6uTnZsmR+\nBG99dpj/fZfP0oUxUkcyC90aHfvzqvByt+e+GyLEyrNgtNo7Nbz56SEAnkydiI0o6bZIvR2Lx8XF\nXbRlpdTqmzvYsEuFh4stNw7ANbzCxXyHORAR5M7RkjpqGtrxEqXzQ864lsUskMFgID2nAjsbJRNG\ne/b+A4LJumFaEHIZbNituqiZlzBw9pwr6x4ucZKBMTsukGA/F3Zkl1FQ2iB1HLNwuLCWji4tCeOG\ni0m0YLQMBgNvfXaYirqeku4xQaKkWzAdH6cdp1ujY+GccJM/qW3MkmIDMRh6dioRhp6YSEvsREUL\n1Q3txEV4Y6UUZ5rNmY+HA5OjfClWN3HspJgQDQaDwcDuIz0npmLCTbes+3wKuYyHbxkLwMpvjoo9\nIwfA3jPNHRPGiSogwXh9u0fFniMVRAS5c8+8MVLHEYQ+O1XVwpbMUgK8nZgtSo4H1bTxw7G2UrDt\ngFos0khATKQlln6054Bu6ljzWD0TruymM+VNG3eJ5lGDoUjdRE1DO5OjfMyqq21ksAfTxg+n8FQT\nOw6WSR3HpGm0evbnVjLM1Y7RgW5SxxGESyo42cAHG/NwdbThd4tjUYr+KYIJWfP9MfQGuHd+hOj9\nM8gc7KyIj/Klou40BScbpY5jccRft8TScyqxVsqZGG48WxUIgycy2INgPxcyjlZQ09AudRyzc65b\n93jT7dZ9OffdEIm1Us6H3+XT0aWVOo7JOlJUy+lOUdYtGK/mti5eWZOFwWDg2cUxosmgYFLyVPXs\nz6siIsiduAjzqAwzdklxPav+Ww+ckjiJ5RETaQmpq1tRV7cSHeaFndj/zSLIZDJuTgxGb+gp2xMG\njl5vYM/hchxslUSHmV+/AS93e26dGUpDSydfbiuSOo7J2nvkbFm3qAISjI9Ob+Cf67Kpa+5k0fVj\nGBdqfu9lgvkyGAz8b1Me0HPyV5ysHBrjR3ni4WLL7sPldGl0UsexKGIiLaF9uT3duqeKAzqLMn2C\nH65ONmzeX0p7p0bqOGbjeGkjdc2dTBnra7b9Bm5LGoW7sy1f7ygWFQ3XQKvTsy+3EndnW8JGiLJu\nwfh88lMBh4tqiYvw5rZZo6SOIwhXZV9uJQWljcSP9SV8pGiON1QUchmzYgJo79SyP1fsKT2UxERa\nQulHK1HIZUwSpS8WxUqpYN7UIE53atl2QHRZHCi7j/SUdU8zw7Lus+xslCyZH0G3Vs/qM2f9hb7L\nKa6jrUPD1HG+yOVipUQwLgeOVbN+cyHe7vb89q6J4m9UMCk6nZ4PvzuGXC4TzfEkcHYf6a1Z4rhy\nKImJtERqGtopVjcxLnQYjvZik3pLc338SJQKORt3q0QX5gGg0xvYe6QcJ3srs99GbuZEf8IC3dhz\npII8Vb3UcUxK+plu3eZ8skUwTTUN7bz+cTZWSjm/XxInjgsEk7P1gJry2jbmTB6Bv5eT1HEsToC3\nE2GBbhwurKG+uUPqOBZDTKQlkiHKui2aq5MNMyf6U1l3mgMF1VLHMXn5J+ppaOkifuxws+9uK5fL\neOiWKADe++YoOnEipk90Oj0ZRytxc7IRJYeCUdFodfxtTRat7Rp+fetYQv1dpY4kCFftu70nkMtl\npKaMljqKxUqKC0BvgB3ZYnePoWLeR5xGLONoJTIZTI7ykTqKIJGbEs9uhVUicRLTt+dst+4JlnFi\nKmyEOzNj/FGVN7M1S3Tp7IvcknpaTncTP9YXhSiZFYzIyg25FKubSIoN4LrJI6SOIwhXraSsCVV5\nM3FjvEWXeQlNn+CHUiFn64FTYk/pISIm0hJobOkk/0Q9EUEeuDnZSh1HkEjQcBfGhQ7jSFEdJytb\npI5jsnQ6Pek5lbg4WjM2ZJjUcYbMvfMjsLFWsPb7Y6JpXR/sPVPWnTDeMk62CKZhe7aaH9JPMtLX\nmUdvGye6HAsmaUtmzwndlEmBEiexbE721kyO8kFd3UaRuknqOBZBTKQlsC+vCoMB4sf6Sh1FkNhN\n08WqdH/lltTT1NbF1LHDUZh5Wff5PFzsuD1pFE1tXXy2pVDqOEZNpzeQcbTnZEtkkIfUcQQBgNLK\nFv7z+RHsbZU8vyQOW2uxDaZgero1OnYcLMPNyYbYMaJ5rtRmn2k6JprZDg3LOeo0IhlnVkbERFqI\njfDB18OBHQfLaG7rkjqOSTrbrXv6BMtrIHXrzFA83ezYsEtFZd1pqeMYrfwTPSdb4i3sZItgvNo7\nNfztw0y6NTqeTI1muKej1JEE4Zrsy62krUNDUmyAeH81AhPDvHB1smHnwTJRrTYExF/8EGtr7yan\nuI7QAFe83OyljiNITCGXccP0IDRaPT9mnJQ6jsnRninrdnOyISLY8lYabawU3HdDJFqdng++zZU6\njtFKP3KmrHucOHkpSM9gMPDv9Ycprz3NLTNCRNNRwaRt3t9T1p0syrqNgkIh54aEINo6NKJabQj0\naSJdWFhIcnIy69atu+i29PR0br/9dlJTU3n77bfPff/ll18mNTWVBQsWkJOTM3CJTVxmfhU6vYGp\nYjVaOCM5LhB7WyXf7T2BRquXOo5JySmqo7W9m4Rxwy22gdS08cOJDPZgX24VR4pqpY5jdPR6A+lH\nK3CytybKgq6h7wvxOS2NjbtV7M2pICLInSXzI6SOI5iIK71ek5KSWLhwIYsXL2bx4sVUVw/NbiDV\nDe0cKa5lzEh3seWVEbnlvGq1iro2qeOYtV4n0u3t7Sxfvpz4+PhL3v7SSy/x1ltv8cknn7B3716K\ni4vJzMyktLSU9evXs2LFClasWDHgwU1Veo7Y9kq4kL2tFSmTRtDY2sWeM2XKQt/sPtOte5oFlnWf\nJZPJePDmKGQyeH9DLjqdOBlzvmMnG2ho6WJKlI/Zb412NcTntDRKyppY/W0erk42/G5xrPibFPqk\nL6/XlStXsnbtWtauXYu399Bcq7w16xQGA1w3WaxGG5MLqtU25kkdx6z1+g5ubW3NypUr8fLyuug2\ntVqNi4sLvr6+yOVyZsyYQUZGBhkZGSQnJwMQEhJCc3MzbW2WfUakrUPDd3tUHDxeQ6CPE37ieijh\nPDdMC0Iugw27SsSWBX2k0erJyK3Ew8WWMRa+L3CovyvJcYGcrGwhbX+p1HGMSrro1n1J4nNaGuu3\nFKLTG3gyNVpsEyT0mTG+XvV6A1uyTmFnoyBhvOWezDZWZ6vV9udVcbiwRuo4ZqvXFpFKpRKl8tJ3\nq62txd395wNYd3d31Go1jY2NREZGXvD92tpaHB0vP3lUq9VoNL1fFK9SqXq9j7EwGAyUVnewN6+B\nQ8VNaLQG5HJIjHK56nGY0rgHkiWNOyrImRxVM6pKD2SyK487ODh4iFIZr8OFNZzu0JAcF4jcQsu6\nz7f4+jHsOVLBuh8KSJzgh6O9tdSRJKfXG0jPqcDRzorxozyljmNU6urqrupz2hw/owdSX8Zd3djJ\nvqOVBHrZ4Wbdhkpl+g0Cxe/70gb6M7ovr9dly5ZRXl5OTEwMS5cuHfSt1I4U1VLb2EHKpEDsbETH\neWMjk8l46OYonv7XTlZuyOXfv50pmsENgiH5y+/LCltAQECv91GpVCYxgWjr0LAjW03avlPn9gf2\n8bDnuskjSJ4UeNV7R5vKuAeapY37nhtc+f3be2jr0FrUuK/Vz2XdYqURwM3ZltTk0fzvu3w+2Xyc\nh24eK3UkyRWqG6lr7mR2XIAooe1Fb5/T5vQZPdD6Ou5N6w9hAO6+PoqQENN/3xK/b+n88vX6xBNP\nMH36dFxcXHjsscdIS0tj7ty5V3yM/p4c+2Z7T5OxSH+lWZ5QMYcxyYApY9zIyG9k3aZspo+9clNW\ncxjztejLuC/3mu/XRNrLy4u6urpzX1dXV+Pl5YWVldUF36+pqcHT07xXAwwGA8dLG/lx30l2H66g\nW6NDIZeRMG44c+NHMC7UU6yaCVcUNsKdT1fMp1wtSnN7063RsS+3Ci83O8IC3aSOYzRuSgwmbV8p\n3+05wY3TgvHxcJA6kqT2nuvWbfqTloH2y89vS/icllJdUwfbs9X4eToyJUo0GxWuTm+v11tuueXc\nvxMTEyksLOx1It2fk2Ot7d0cPZGHv5cjSVOjBn31e6gZw8mSgfKo53AO/20raQdq+VXKeJwuU61m\nTmO+Gv0dd79O0fv7+9PW1kZZWRlarZbt27eTkJBAQkICaWlpAOTl5eHl5XXFsm5T1tahYdMeFU+8\ntoNn39rN1iw17s42LJkfweoXruP3S+KYMNpLTKKFPrGxUkgdwSRkF9TQ0aVl2ng/s/sA7w8rpYI7\nk0eh0xvOrdhbKoOhp6zb3lbJhNFigvhLlvQ5bQw27CpBqzPwq1mh4nhAuGpXer22trbywAMP0N3d\nDUBWVhajRo0a1Dw7D5ah0epJmRQoPoONnJuTLQtSwmht1/BxWoHUccxOryvSubm5vPLKK5SXl6NU\nKklLSyMpKQl/f39SUlJ48cUXWbp0KQDz5s0jKCiIoKAgIiMjWbBgATKZjGXLlg36QIaawWDg2z0q\nPvzu2M+rz+OHM3eKWH0WhMF2trv5dAvu1n05U8YO5+0vjrD7cDl3zB4tdRzJFKmbqGnsYGaMP1ZK\ncYLqlyZOnGj2n9PGorW9mx8zTuLubMusGH+p4wgm6FKv16+++gonJydSUlJITEwkNTUVGxsbIiIi\nel2N7q/NmaeQy2XMiu19VVuQ3o3Tg/lx30m+Tz/J9fEjCfRxljqS2eh1Ih0VFcXatWsve3tcXBzr\n16+/6PvPPPNM/5IZMY1Wzztf5fDT/lJcHW2467owZscFXPW1z4IgXL3Obi2ZeVX4eNgT4u8idRyj\n42hnRXSYF1n51ZTVtFrs3p5nu3VPE2Xdl2XOn9PG5Lu9J+js1rFwTrg4qSNcs1++XsPDw8/9e8mS\nJSxZsmRIcpSUNaEqb2ZypI847jURVko5D94UxfIP9vP+hlz+8nC8qCQYIKL7ylVqbuviz++m89P+\nUkL8XXjj6RncnjRKvJkIwhDJPlZDZ7eO6RNEWfflnF2p33PmGmFLYzAY2JtTgZ2Nguiwi7duFISh\n0tmt5dvdKhzsrJgzZYTUcQSh37Zk9jQZu26y+Hs2JXER3kwY7cmhwlqyjlVLHcdsiIn0VSitamHp\nm7vIU9WTMG44f//NNIa5in0gBWEo7RZl3b2aHOmDlVLOHgu9TlpV3kxVfTtxET5Yi74DgoS2ZJ6i\n5XQ3NyQEYW9rJXUcQeiXbo2OHQfLcHOyISZcnKQ0JTKZjAdvjkIul7FqQy4arV7qSGZBTKT7KCu/\nimf/vZvqhnbuui6M3y2OxVbsmycIQ6qjS0tWfjV+ng6M9BXX+FyOva0VMeFelFa1cqqqReo4Q25v\njujWLUhPq9Pz9Y5irK0U3Djd8rrhCuZnX24lbR0akmIDxJ7EJmiEjzPz4kdSUXeaTXssc6urgSZe\nBb0wGAx8tb2Y5R/sR6fT87vFsSycEy6aiQmCBLLyq+jW6Jgmyrp7NW28ZZZ3GwwG9hypwMZawUSx\nYiJIaPfhcmoaO7huUiAujjZSxxGEftu8v6esO3lSoMRJhGu1cG44TvZWfLr5OE2tXVLHMXliIn0F\nGq2ON9cfYvWmPNycbJNFf8IAACAASURBVPn749NEOakgSOjslk7iddi7SZE+WCvl7D5cjsFgkDrO\nkDlZ2UJl3Wnixnhjay2qhgRp6PUGvthWhFwu45aZoVLHEYR+q25o50hxLRFB7hbbxNIcONlbs3BO\nOO2dWtb9eEzqOCZPTKQvo6m1iz/+N52tWWpGBbjy+lOJjApwkzqWIFis9k4N2QU1BPo4MUJs3dAr\nOxslsRHelNW0UVrVKnWcIXOurHu8KOsWpHOgoJpTVa0kRvvh7W4vdRxB6LetWacwGCBFrEabvJ4t\nsJz4aX8pJWVNUscxaWIifQknKppZ+uZOjp1sIHGCH397bBoeLqKpmCBIaX9eFRqt/lzJstC7syv3\nuy2o6Vh6TgXWVgpiwr2ljiJYsC+2FgFw26xREicRhP7T6w1syTqFnY2CBPEZbPIUCjkP3RyFwQAr\nN+RaVNXaQBMT6V/Yl1vJ797aTU1jB4vmhvPMohhsRNdXQZDcvtxKAKaJlcY+iw33xsZawR4LKe8u\nrWpBXd1GTLgXdqIZpCCRPFU9x042EBfhLZoiCmbhSFEttY0dTBvvJ95bzcSE0V5MjvQhT1V/rpJL\nuHpiIn2ejbtLePl/mRiA3y+JIzUlTDQ0EgQjoNcbyC2pZ5irHf5ejlLHMRm2NkrixnhTUXeaExXm\n3707/Yjo1i1I74ttPavRtyeJ1WjBPIi9o83T/TdFolTIWP1tHt1iO6xrIibSZxSeamTVhlzcnGx5\n5bFp4kBMsDgvv/wyqampLFiwgJycnAtuS09P5/bbbyc1NZW33357yLOpq1tpOd3N2BAPcXLrKllS\neffenAqslHLiIkRZtyCNk5UtHDhWzZiR7kQEeUgdRxD6rbW9m4zcSvy9HAkbIXoFmZPhwxy5aXoI\nNY0dbD9cJ3UckyQm0vRsMP+vTw+hN8Azd8cQ4u8qdSRBGFKZmZmUlpayfv16VqxYwYoVKy64/aWX\nXuKtt97ik08+Ye/evRQXFw9pvtySnjf4qJBhQ/q85iBmjDd2Ngr2HDHv8m51dSulVa1MDPPC3tZK\n6jiChfpy+5nV6NliNVowDzsPlqHR6kmZNEKcyDZDqSmjcXW0YUt2DRV1bVLHMTliIg18uvk46upW\n5icEMTZUHKgLlicjI4Pk5GQAQkJCaG5upq2t5w1VrVbj4uKCr68vcrmcGTNmkJGRMaT5jpbUAzBW\nTKSvmo2VgkkRvlTVt1Nsxt05RbduQWrVDe3sOlTOCB8nYkWzO8FMbN5/CoVcxqxYf6mjCIPA3taK\ne2+IoFtr4Jk3d3G4sEbqSCbF4jsGFJ5q5MttRXi527NkfoTUcQRBEnV1dURGRp772t3dndraWhwd\nHamtrcXd3f2C29Rqda+PqVar0Wg0vd5PpVJd8XaDwcCRompcHa1ob65C1WIeZ8R7G/dAGuWjYCew\naWc+N0/1HbLn/aXBGPPJqna2HKzl6IkWrBQyvOw7h/S/bV/0lic4OHiIkgiD6Zsdxej1Bm5LGoVc\nbh7vU4JlK6vtQFXRzJQoH9ycbKWOIwyS2XGBVFbV8OXuSpa9l8GS+RHcOjNUVCD0gUVPpDVaHW+u\n7ynpfuLOCaIToSCcMRAlwAEBAb3eR6VS9TqJOFXVQtv/Z+/O46Ku9sePv4Zh3xl2BBRwQVFU3EXc\nkjRNW27mkmZ7Vtpmi9fuzX63suWa3dLut5upZStpWmaWWmJuKO4KLgijbCo7yM4A8/uDpMwFFIbP\nLO/n4+EjZv28T8NhPu/POed9KpMYHuVPWFhYi+MyBs1pd2sKDKrjy/hsks6U8/Q9IYp8ObZmm/V6\nPftP5LJ6yymStQ2zFToHu3Pv2G5EdPJulWO0lrb+rIUyikur2bQnHR8Ph8a6BEKYut3HCwGI7S9F\nxszd4AgNfSNDeeOTvaxYf4xTmcU8Nak39pIbXVOz/u8sWLCAw4cPo1KpmDdvHpGRkQDk5OTw3HPP\nNT4vMzOTOXPmoNPpeO+99wgObti0ffDgwTz22GMGCL9lvt6cQsb5Um4Z3IGeRnbyJURb8vHxIT//\nj0ITubm5eHt7X/GxnJwcfHx82iy2i9O6ZX30jbO1UTMgwo/4/VmkZBTRpb2m6RcZobq6erYfyubb\n+FTOnGuoQh4V7sNdIzrRXQrRCQWt36GlpraeO4Z3xFotq+ZE67vauTg0FARdtGgRarWaoUOH8sQT\nT7T4eDW6OvalFOPhYkef8Lb7zhfKCW+v4T/PDOPNlXvZcfgsmTmlzLu/PwFeslvK1TSZSP+5CFFa\nWhrz5s0jLi4OAF9fXz777DMAamtrmT59OiNHjmTjxo2MHTuWF1980bDRt0BqZjGrt5zCx8OB+2RK\nt7Bw0dHRLF68mMmTJ5OcnIyPjw/Ozg1/OAMDAykrKyMrKws/Pz/i4+NZuHBhm8V2sdBYjzCpgNsS\nMb3aEb8/i+2HzppcIl1VU8vmPRl891squUWVWKlgWO9A/jayIyEBbkqHJyxcVU0d63eextXJllH9\ng5UOR5iha52LQ0NB0GXLluHr68u0adMYPXo0HTt2bNExE46eo7K6nrGDg1DLxSGL4eFqz2szo1m2\nLokfd57m2f9s47l7+tC3q9R9uJImE+mrFSG6eJJ90dq1axk9ejROTk6GibQV6Wrr+c/XB6iv1/Pk\n3b2lwquweFFRUURERDB58mRUKhXz589nzZo1uLi4EBsbyyuvvMKcOXMAGDt2LCEhIW0Sl17fsH+0\nxtUefy/j/9tizHp19sHJwYadh7N5YHyESazhvFBew487T7N+h5YL5TXYWlsxLjqE24eF4ecpvw/C\nOOxKLqS8Use0MeHY28o0SNH6rnUu/ueCoEBjQdCWJtJb9jXUQomVvaMtjo21FTPvjKRjoDv//fYw\n/1q2m2ljujLxpk4y8+svmvyLf60iRH+2atUqli9f3ng7MTGRBx98kNraWl588UW6dbv2qG9rFSZq\njg17ckg/X8rgCA0u1qVotaUtfk9DM7biOW1F2n1lhlhz+edlGgDh4eGNP/fr1++Sq99tJSu3jOKy\naob1DpQ/3i1kY23FoO7+/LI3gxPphUa9x219vZ7Pfz7Ouu1aqmvqcHawYdKoztw6JBR3FzulwxOi\nka62jq2H83GwUzMuum0uMArLY4iCoE3xdLOnf7gH7bxlWq+lGtU/mPb+Liz4ZC+f/XSc1Kxinp4s\nA5B/dt2XTq9UhOjgwYOEhoY2Jtc9e/ZEo9EwfPhwDh48yIsvvsgPP/xwzfdtrcJETUnNKmbzgSS8\nPRx4aupAk/hlsNRiNdJu8cf+0cab9JmSIb0C+GVvBjsOnzXqRHrboWxW/XoKTzd7po3pyuiB7aUY\npDBK8fuzKCmv5fZhYTg72iodjrAQrVEQtKkBrFv7uQFuMqBhQa7UZjXw9B0d+GRTBglHz6HNKuTB\nW9rj62E+F7Wb81lf7by8yTOTaxUhumjr1q0MGjSo8XZYWFhjdd3evXtTWFhIXV0darW6yUANSVdb\nz3tfH6S+Xs/sib1MIokWwpI17h8t+7u3ip6dvHFxbJje/dCE7kY5vVtXW88XPx/HWq3irVkx+Goc\nlQ5JiCuqqq7lq40nUFupuH2YeewoIIyTIQqCttUAlimyxHY31eaFXTuyYv0xvt+Wxn/WaJkztQ/9\nI/zaMELDaOln3WT1gOjoaDZu3AhwWRGii44ePXrJNNClS5eyfv16AFJSUtBoNIon0QCrfk3hzLkL\njB7Ynt5dpAKhEMZMr9dzNC0fjasdAbI+ulVYq60Y1COAwgvVHDtdoHQ4V7Q5MZ3zBRWMGdRBkmhh\n1L75NYX8kipG9vbC081B6XCEGbvWufifC4LW1tYSHx9PdHS0kuEKM6RWW/HQbd2Zc08fauv0vLp8\nD9/9lqp0WIprckS6qSJEAHl5eXh6/jFNcPz48Tz//PN8/fXX1NbW8vrrrxuuBc2kzS7hm19S8HJ3\n4IHxEU2/QAihqOy8MopLqxnaq52sj25FQ3oGsGlPOjsOnzW6LcWqamr5etNJ7G3V3D2qs9LhCHFV\nZ/PKWLs1DS93B2L7yIV5YVjGWhBUWJ7hUYEE+7rw6rLdLFuXjLeHI9GRAUqHpZhmLTq7VhEi4LL1\nz35+fo3bYhmD2rqGKt11MqVbCJPRuH+0TOtuVZEdvXB1smXnkbM8fHsP1EY0vfuH7VqKSquZNKoz\nHi72SocjxBXp9Xr+991RauvqeWhCd+xsqpQOSVgAYywIKixTaDs3Xn5oIC8s3s67Xx3AT+NIWKC7\n0mEpwiI2hlv16ylOn71AbP9gomRTeSFMQmOhsVDjLYplitRqKwZHBlBcWk2yNr/pF7SRsooavo1P\nxcXRhjuGt2zbFiEMaU/yeQ6cyKVnJy8GR/orHY4QQrS5kAA3nrunDzW6Ol5bvofCC5Z5QdHsE+nT\nZ0uI23wSLzd7HpzQXelwhBDN0LB/dD7uLnYE+sjWG60tplfDNKzth84qHMkfvo1PpbxSx10jO+Pk\nILOGhHGq1tWx9Psk1FYqHr0jUpadCCEs1oDu/tw7thv5JVUsWJFIja5O6ZDanFkn0g1Tug9SV6/n\niYm95ORMCBNxNr+cwgvV9AjzkhNVA4gI9cLdxY5dR85SV1evdDgUXqhi3XYtnm72jBsia/vaQmJi\nIoMGDSI+Pl7pUEzKmi2nyC2sYMLQMIJ8XZQORwghFPW3ER0Z0SeQkxlFLP7mUKtszWZKzDqR/nbL\nKbTZJYzqF0zfrr5KhyOEaCbZP9qw1FYqoiMDuFBew5FU5ad3f735JDW6Oqbc3AU7G+V3eDB3GRkZ\nrFixgqioKKVDMSnnC8pZveUUGlc7JsdKMTwhhFCpVMya2Isu7T3YeiCL1VtOKR1SmzLbRPp8QTlf\nbz6JxtWeB2+TKd1CmJKjqb/vH21kVaXNyZCeDdO7dxxWdnr3ufxyNu1OJ8DLiVH9ghWNxVJ4e3uz\nZMkSXFxkRPV6fPx9EjW19dw/vrsULRVCiN/Z2qh56f7+eLk7sHLDcRKOnlM6pDbTrKrdpujHnaep\nrdMzY1w3nGVKtxAm4+L+0e7Osj7akLqGeKJxtSPh6Fke+1sk1mplrqt+8fMJ6ur1TLulK2qFYrA0\nDg7Xv+dxZmYmOp2uyedptdobCcnoHUsvZU/yecL8HQlyq76sneba7qZIu68sNDS0jSIRwjh4uNjz\nzwcG8MKS7Sz6cj9vz44hJMBN6bAMziwT6aqaWjYnZuDubNdYVEcIYRrOFZRTeKGK6J4Bsj7agNRW\nKqJ7tuOH7VoOn8qjT3jbL385fbaEbYeyCG3nZtH7UBrSqlWrWLVq1SX3zZ49m5iYmOt6n6CgoCaf\no9VqzTKB0NXW8WZcPFZWKp6a2v+yk0NzbXdTpN1CiD8LbefGnKlRLPhkL/9atodFTw81+60szTKR\n/u1ANuWVOiaN6oyNtay3E8KUyLTutjOkZwA/bNey49BZRRLpz346jl4P947tipUR7WdtTiZOnMjE\niROVDsOkffdbGufyyxkfE2oRIyxCCHGjBvUIYPotXfnsp+MsWJHI649FY2vGtU/Mbh6dXq9nw87T\nWFmpGDOog9LhCCGukxQaazvh7TV4udmTkHQOXW3bVu8+drqAvcdy6B7mSVQXnzY9thDNlVdUSdwv\nKbg72zF1dLjS4QghhNGbeFMnhvUO5ER6EUtWmXclb7NLpI+fKUR7toRB3f3xcr/+dWBCCOVc3D/a\n1cmWYNlaxuCsfp/eXV6p41BKbpsdV6/X8+mPxwCYMbabTOFvY1u3bmX69Ols376dRYsW8cADDygd\nktFa9kMS1TV1Um9FCCGaSaVSMXtSLzoHuxO/P4tv41OVDslgzG5q9487TgPIXqRCmKDzBRXkl1QR\nHSnro9vK0N7t+H5bGt/9lkbfrr5t8v99/4lcjp0uZECEH+EdNAY/nrjU8OHDGT58uNJhGL3DKXns\nPHyWLu09GNm36TXiQgghGtjZqHnp/gHM+c9vrNxwjCAfZwZ091c6rFZnViPShReq2HnkLMF+LnQP\nlWmhQpgamdbd9joFudMn3IcjqfnsOmL4LSvq6/Ws3HAMlQqm39LV4McT4kbU1tXzv++OoFLBzDsi\nZQ2/EEJcJ42rPS89MAAbazULv9jP6bMlSofU6swqkd6YcIa6ej23RofIaJYQJujo74m0FBprOyqV\nikdu74G12oqP1yVRVVNr0ONtP5TN6bMXGB4VSHt/V4MeS4gb9cN2LZk5ZYwZ2IGOQe5KhyOEECap\nY6A7z06NoqqmjteW76GsokbpkFqV2STStXX1/Lz7DI721gzvI1OwhDA1DftHF+DqZEuQrI9uUwHe\nztw+LIz84kpW/3rKYMepravni59PYK1WSeEmYbQKL1Tx1aYTuDjaME1mTQghRItERwYwObYLuUWV\nLFl92KyKjzUrkV6wYAGTJk1i8uTJHDly5JLHRo4cydSpU5k+fTrTp08nJyenydcYQsLRcxReqGZU\nv2Ac7Mxu6bcQZi+nsIL84koiQj1lGqUC7h7VGU83e76NT+VcfrlBjrF5TzrnCsoZPbADfp5OBjmG\nEC21Yn0yldV1TB/bDVcnW6XDEUIIkzc5tjPdQjTsPHyWX/dmKB1Oq2ky40xMTCQ9PZ24uDjS0tKY\nN28ecXFxlzxn6dKlODk5XddrWtuPOxuKjI2NliJjQpiiJJnWrSgHO2seGB/Bvz/fz8ffJ/HPBwe0\n6vvX6Or5enMqdrZqJo3q3KrvLURrSdYWsHV/Fh0D3bh5QHulwxECnU7H3LlzOXv2LGq1mjfeeIOg\noEtnXkZERBAVFdV4+5NPPkGtNt+9e4XpUautmDO1D0++E8//1h6lW4gnAd7OSofVYk2OSCckJDBq\n1CgAwsLCKCkpoaysrNVf0xKnz5aQrC2gd2dv2pnBhyKEJTqaVgBIoTElxfRqR/cwTxKPnWff8ZxW\nfe9tRwsovFDNhJhQPFztW/W9hWip2rp6dh05y/txBwF49M5I1DIzRhiB9evX4+rqyldffcXMmTN5\n5513LnuOs7Mzn332WeM/SaKFMfLROPL4XT2pqqnj31/sR1dbr3RILdbkiHR+fj4RERGNtzUaDXl5\neTg7/5Gwzp8/n+zsbPr06cOcOXOa9Zq/yszMRKfTNRmwVqu97L6v47MA6NvR8YqPmwNzbVdTpN1X\nFhoa2kaRtJ2ktHxcHG1o7ycFqJSiUql49I5Inlq0lY++O0rPTl7YWLf8hKysooZfDuTi7GDDnSM6\ntUKkwtgcO13Aoi9TeG6au0ltaXa+oJxNe9LZnJhBcWk1ALcPCyO8vem0QZi3hIQEbr/9dgAGDx7M\nvHnzFI5IiBs3tHcg+0/ksmVfJl9uPMGMcd2UDqlFrnsx8V8XiD/55JPExMTg5ubGE088wcaNG5t8\nzZX8dZrKlWi12ssSiLKKGg6kHsNH48i4ET3N8gryldptCaTdliOnsILcokoG9fCX9dEK6+Dvyrjo\nEH7YruW739KYeFPLpmHX1tXz/jeHqKyu575x4Tg72LRSpMKY2NmoyS2u5s2Ve3nv2eG4OdspHdJV\n6Wrr2ZN8jo0J6Rw6lQeAs4MNE2JCuXlge7mYJ4xKfn4+Gk3DhR0rKytUKhU1NTXY2v6xfr+mpoY5\nc+aQnZ3N6NGjuf/++5t835YMYFkCS2x3W7V5dG8XDqfY8u2WU/i51NIpUNnZxM1p99XOy5tMpH18\nfMjPz2+8nZubi7e3d+Pti1fJAIYOHUpKSkqTr2lNv+zNpLqmjnGDO5hlEi2EJWjcP1r2fzcKU0eH\ns+1gFnG/pDCiTxBe7g439D51dfUs+vIACUfP0amdE+NjLOsCkSUJC3Rn3ABf1u/O4Z0v9jP/4UFG\n9518Nq+MTXvS+WVvBiVlDVuwRIR6MmZgewZFBmBnI9NhhbJWrVrFqlWrLrnv8OHDl9y+0uDUCy+8\nwIQJE1CpVEybNo2+ffvSo0ePax7rRgewLIEltrut2/z3+zx5YckOvtp6jsXPjcDFUZnCji1td5Nr\npKOjoxtHmZOTk/Hx8Wmcol1aWsqDDz5ITU3DF9LevXvp1KnTNV/Tmurr9WzYeRpbaytG9ZeiIEKY\nqsb9oztKoTFj4Oxgw4yx3aiuqWP5D8k39B519XreizvI9kPZdAvR8PC4DthKomLWborypm9XXw6m\n5PHNLylKhwOArraO7Qezeen/dvLom7/ybXwq9fUN07f/+8JI3nxiCMP7BEkSLYzCxIkT+eabby75\nd8cdd5CX1zBzQqfTodfrLxmNBpgyZQpOTk44OjoycOBAUlKMo/8JcTVd2muYOroLBSVVLP7mkMlu\nidXkiHRUVBQRERFMnjwZlUrF/PnzWbNmDS4uLsTGxjJ06FAmTZqEnZ0d3bp1Y8yYMahUqsteYwgH\nTuZyrqCcUf2CZYsKIUzY0bQCnB1kfbQxualfMD/vPsP2Q9ncMqjDdV3kqK/X88GqQ8Tvz6JLew/m\nPzSQ82czDRitMAZWKhXPTIni6Xe38tWmE3Tt4EGvzj6KxKLX69m0J4OVG45xobzhYn9kRy9uHtCe\nQT385aKOMBnR0dH8/PPPxMTEEB8fz4ABl+6ooNVq+eCDD1i4cCF1dXUcOHCAMWPGKBStEM1318jO\nHDyZR8LRc2zak8HogaY3KNqsNdLPPffcJbfDw8Mbf54xYwYzZsxo8jWGcHHLq3FDZMsrIUxVbmEF\nuYUVDIjwk/XRRsTKqqHw2HPvb+N/a4/wn2eHY61uchITer2eD9ceYXNiBh0D3Xjl4UE42su6aEvh\n6mTL3Hv78eKS7Sz8Yj/vPTscT7cbWxpwo/KKKlmy6hAHTubiaG/NncM7cvPA9rKrhzBJY8eOZdeu\nXUyZMgVbW1vefPNNAD766CP69etH79698fPz46677sLKyoqRI0cSGRmpcNRCNE1tpWLO1D7Mfiee\npd8fJSJUQ6CPi9JhXZfrLjZmLM7ll7P/RA7h7T3oGOiudDhCiBuUpJVp3caqc7AHsf3bs2lPOht2\nnmbC0LBrPl+v1/Px90n8tOsMIQGu/OvRwVJczAJ1DvbggfHd+ei7o7z92T5efyy6WRdhWkqv17M5\nMYNl65KoqKolKtyH2RN73fAafyGMwcW9o//qkUceafz5+eefb8uQhGg13h4OzJrYk7dW7mPhF/v5\n9+yh2Fgb/vuitZhOpH+xYddp9HoYN8SyigEIYW6Opv6+f7QUGjNK947tipODDV9sPEFRadVVn6fX\n6/n0x2Os264l2M+FVx8drFjxEKG8W4eEEN0zgGOnC/n8p+MGP15+cSWvfLybxd8cAuDJu3vxykMD\nJYkWQggjN6RnO0b1CyYtq6RNvi9ak0km0lU1tWxOzMDdxY7oyAClwxFCtECSNh8nBxs6BLgpHYq4\nAjdnO6aNCaeiqpaVP179C+6LjSf4Nj6Vdt5OvPboYKPe/kgYnkql4sm7exHg5cS38ansSTpnkOPo\n9Xo270nniX9v4cCJXKK6+LDkuZHEDmiPSiVLRYQQwhQ8ckcPArycWLM1lcMpeUqH02wmmUj/diCb\n8kodowe0N6nhfyGMkU6nY86cOUyZMoVp06aRmXl5UaiIiAimT5/e+K+urq5Vjl1YWsP5ggq6h3oa\n3VY54g+3DOpAB39Xftmbwcn0wssej/vlJHGbU/D3dOL1x6LxcLVXIEphbBztbZg7ox+21la8+/VB\nzheUt+r7XxyFfv+bQ+j1MGtiL155eCDeHjIKLYQQpsTBzpo59/RBbaVi0VcHGotEGjuTy0L1ej0/\n7tRiZaVizKAOSocjhMlbv349rq6ufPXVV8ycOZN33nnnsuc4Ozvz2WefNf5Tq1un4m3a2YYT6+5h\nMq3bmKnVVjx6R8OepB+uPUp9/R/bVKyJT+Xzn07g4+HAa48NbvPCUsK4hQS4MfPOSMordbz12T50\ntS2/CKfX6/klMZ1Zv49C9+rszZLnRzB6oIxCCyGEqeoc7ME9Y8IpvFDF4m8OmsSWWCaXSB8/U8jp\nsxcY1N1f1j4J0QoSEhKIjY0FYPDgwRw4cKDNjp2afTGRlkJjxq57mBdDe7cjNbOYzYkZAKzbnsaK\n9cl4udnz+mPR+Hg4KhylMEaxA9pzU78gUjOL+fj7pBa9V0FJJf9atof34g5Rr4dZE3vyr0cGye+e\nEEKYgTtHdKJHmBe7k86zcXe60uE0yeSqdv+4Q7a8EqI15efno9FoALCyskKlUlFTU4Ot7R+Fompq\napgzZw7Z2dmMHj2a+++/v1WOnZpdjpO9NSGyPtokPDA+gsTk83z64zFKK2r49MdjaFzteP2xaPw8\nnZQOTxixmXdGkpZVwoZdZ4gI9WRo78Dren12Xhm7jpzl2y2nKK+qpVcnb2ZP6iUJtBBCmBG1lYpn\np0Yxe2E8S79PoqJKR2RHb0LauRnlEkCTSqRLynXsPHKW9n4uUuFXiBuwatUqVq1adcl9hw8fvuT2\nlabSvPDCC0yYMAGVSsW0adPo27cvPXr0uOaxMjMz0el0V328uExH/oUaIjq4kH7m9HW0wjxotVql\nQ7ghsX28+SGhIZl2drDm0XHBVJXmoi3NbfK1ptrmlmqq3aGh5r/7hL2tNS/e25dn//Mbi785REiA\nG0G+V98vVK/Xk5pVzO6k8yQcPUdmTikADnZqnrirp0zjFkIIM+Xl7sCTk3rz1sq9rFh/DAAnBxu6\nh3oS2dGLyE7eBPu6YGUEibVJJdK7kgupq9czLjpEvkCFuAETJ05k4sSJl9w3d+5c8vLyCA8PR6fT\nodfrLxmNBpgyZUrjzwMHDiQlJaXJRDooKOiaj2/d31DUbECPYItIJP5Mq9WabJvvD27PkdO/UXih\nmgWPR9PB37VZrzPlNreEpbb7SgJ9XJh9d2/e/mwfb67cyztPDsXe7o/TkLq6epJPF5Bw9By7k86T\nX1wJgK21FQMi/BjY3Z8B3f1kWzUhhDBzg3r4s+wfsRxNzefI7//2JJ9nT/J5AFydbOkR5kWPjl5E\ndvQi0MdZkdzQnIMR3AAAIABJREFUZBLp2rp6diUX4mhvzfA+1z5BF0I0X3R0ND///DMxMTHEx8cz\nYMCASx7XarV88MEHLFy4kLq6Og4cOMCYMWNafNyjaQ37R/eQ9dEmxcZazcInhwJckgQJ0Rwxvdpx\nTFvA+p2n+b81R3j8rp4cOplLQtI5EpNzKK1oqNTq5GDD8D6BDOruT1QXH/ldE0IIC+Pp5sDwPkGN\neV9uYcXvSXUeR1Pz2XnkLDuPnAXAw8WOHh29CPZzwdneBidHW5wdbHB2sMHpT/+1tWmdYrkXmcw3\n0+6kc1yoqGVCTCgO8oUqRKsZO3Ysu3btYsqUKdja2vLmm28C8NFHH9GvXz969+6Nn58fd911F1ZW\nVowcOZLIyMgWHzcpLR97WytC2sn6aFMjSY1oiQcmRHAyo4gt+zLZcSibmtp6ADSu9owd3IFBPfzp\nHuaFtdrk6qEKIYQwEB+NI6P6BzOqfzB6vZ5zBeWXjFhvO5jd5HvYWls1JNaONjjZ26BxUjE35MZn\nOpvM2VB9vR5nB2tuHSJT5IRoTWq1mjfeeOOy+x955JHGn59//vlWP66Loy0hvnZGWTxCCGE4NtZq\n5t7bjxeWbMfeVs3A7v4M6uFPpyAPo1jzJoQQwripVCoCvJwJ8HJm9MAO6PV6snLLyCuupLxSR1ml\nruG/FTWUV9U2/PdP95eU1XA2rxwXR2tqauuxu8GRapNJpIf2DiTQrQZ/L6kMK4Q5+PeTMZw+bXlF\nxoQQDSMLn7w8WukwhBBCmAGVSkWQr8s1i1j+lV6vJ02rveEkGkwokRZCmBcpGCiEMmpra3nppZfI\nyMigrq6OF154gb59+yodlhBCCNFmVCoVVi08F21WIr1gwQIOHz6MSqVi3rx5l6yP3L17N4sWLcLK\nyoqQkBBef/119u7dy1NPPUWnTp0A6Ny5M//85z9bFKgQQgghWu7777/HwcGBr776ilOnTvH3v/+d\n1atXKx2WEEIIYVKaTKQTExNJT08nLi6OtLQ05s2bR1xcXOPjL7/8MitXrsTPz48nn3yS7du3Y29v\nT//+/Xn//fcNGrwQQgghrs+ECRO49dZbAdBoNBQXFysckRDiWhITE3nqqadYsGABI0aMuOzxdevW\n8emnn2JlZcXdd9992TaXQgjDaDKRTkhIYNSoUQCEhYVRUlJCWVkZzs7OAKxZs6bxZ41GQ1FREf7+\n/gYMWQghhBA3ysbGpvHnTz/9tDGpvpbMzEx0Ol2Tz9NqtS2KzVRJuy1LU+1uzb3jMzIyWLFiBVFR\nUVd8vKKigg8++IDVq1djY2PDXXfdRWxsLO7u7q0WgxDiyppMpPPz84mIiGi8rdFoyMvLa0yeL/43\nNzeXnTt38tRTT5GSkkJqaiozZ86kpKSEWbNmER0dfc3jyJf0tUm7LUtbfkkLIczXqlWrWLVq1SX3\nzZ49m5iYGL744guSk5P58MMPm3yfoKCgJp+j1Wot8m+TtNuytHW7vb29WbJkCS+99NIVHz98+DA9\nevTAxaWhyFJUVBQHDhxg5MiRbRajEJbquouN6fX6y+4rKChg5syZzJ8/Hw8PDzp06MCsWbO45ZZb\nyMzM5N5772XTpk3Y2tpe9X2b8yUthDAvlngSBpbZbktsMyjf7okTJ15xmueqVavYsmUL//3vfy8Z\noW4JpduqFGm3ZWnrdjs4OFzz8fz8fDQaTePtiwNerUE+Y8thiW2Glre7yUTax8eH/Pz8xtu5ubl4\ne3s33i4rK+Phhx/m6aefZsiQIQD4+voyduxYAIKDg/Hy8iInJ0eSZSGEEEJhmZmZfP3113z++efY\n2dkpHY4Q4nfXmkHSXFca8BJCGEaTiXR0dDSLFy9m8uTJJCcn4+Pj0zidG+DNN99kxowZDB06tPG+\ndevWkZeXx4MPPkheXh4FBQX4+voapgVCCCGEaLZVq1ZRXFzMI4880njfsmXLrjlrTAhheFebQXIt\nVxrw6tWrV2uHJoS4ApW+GZeuFi5cyL59+1CpVMyfP59jx47h4uLCkCFD6NevH71792587q233sq4\nceN47rnnuHDhAjqdjlmzZjFs2DCDNkQIIYQQQghzNHfuXEaPHn1Z1e6qqirGjx/Pt99+i1qt5s47\n72T16tWNa6aFEIbTrERaCCGEEEII0ba2bt3KsmXL0Gq1aDQavL29Wb58OR999FHjYNbPP//MsmXL\nUKlUTJs2jQkTJigdthAWQRJpIYQQQgghhBDiOlgpHYAQQgghhBBCCGFKTCaRXrBgAZMmTWLy5Mkc\nOXJE6XDaxJ49exg4cCDTp09n+vTpvPrqq0qHZFApKSmMGjWKzz//HIBz584xffp0pk6dylNPPUVN\nTY3CERrGX9s9d+5cxo8f3/i5b926VdkADUD6s/n3Z7DMPm2J/RmkT1tCn7bE/gzSpy2lT1tafwbL\n7NOt3Z+vex9pJSQmJpKenk5cXBxpaWnMmzePuLg4pcNqE/379+f9999XOgyDq6io4NVXX2XQoEGN\n973//vtMnTqVW265hUWLFrF69WqmTp2qYJSt70rtBnj22WcvKyhiLqQ/m39/Bsvs05bYn0H6tCX0\naUvszyB92tL6tKX0Z7DMPm2I/mwSI9IJCQmMGjUKgLCwMEpKSigrK1M4KtGabG1tWbp0KT4+Po33\n7dmzh5tuugmAESNGkJCQoFR4BnOldps76c+WwRL7tCX2Z5A+bQkssT+D9GmQPm2uLLFPG6I/m0Qi\nnZ+fj4eHR+NtjUZDXl6eghG1ndTUVGbOnMmUKVPYuXOn0uEYjLW1Nfb29pfcV1lZ2bivqaenp1l+\n5ldqN8Dnn3/OvffeyzPPPENhYaECkRmO9Gfz789gmX3aEvszSJ+2hD5tif0ZpE9fZCl92lL6M1hm\nnzZEfzaJqd1/ZSmFxjt06MCsWbO45ZZbyMzM5N5772XTpk2Nv+SWxFI+c4DbbrsNd3d3unbtykcf\nfcSSJUt4+eWXlQ7LYCzls5X+fClL+dwtrT+D5Xy20qf/YCmfOUifNlfSny9lCZ85tLw/m8SItI+P\nD/n5+Y23c3Nz8fb2VjCituHr68vYsWNRqVQEBwfj5eVFTk6O0mG1GUdHR6qqqgDIycmxmKlVgwYN\nomvXrgCMHDmSlJQUhSNqXdKfLbM/g2X2aXPvzyB92lL7tCX2Z5A+ba4svT+DZfbplvZnk0iko6Oj\n2bhxIwDJycn4+Pjg7OyscFSGt27dOpYtWwZAXl4eBQUF+Pr6KhxV2xk8eHDj575p0yZiYmIUjqht\nzJ49m8zMTKBhvUqnTp0Ujqh1SX+2zP4Mltmnzb0/g/RpsMw+bYn9GaRPmytL789gmX26pf1ZpTeR\nsfuFCxeyb98+VCoV8+fPJzw8XOmQDK6srIznnnuOCxcuoNPpmDVrFsOGDVM6LINISkrirbfeIjs7\nG2tra3x9fVm4cCFz586lurqagIAA3njjDWxsbJQOtVVdqd3Tpk3jo48+wsHBAUdHR9544w08PT2V\nDrVVSX827/4MltmnLbU/g/Rpc+/TltifQfq0JfVpS+rPYJl92hD92WQSaSGEEEIIIYQQwhiYxNRu\nIYQQQgghhBDCWEgiLYQQQgghhBBCXAdJpIUQQgghhBBCiOsgibQQQgghhBBCCHEdJJEWQgghhBBC\nCCGugyTSQgghhBBCCCHEdZBEWgghhBBCCCGEuA6SSAshhBBCCCGEENdBEmkhhBBCCCGEEOI6SCIt\nhBBCCCGEEEJcB2ulAxBCmI63336b/fv3U1tby6OPPsrNN9+sdEhCCCGEEEK0OUmkhRDNsnv3bk6d\nOkVcXBxFRUXccccdkkgLIYQQQgiLJIm0EKJZ+vXrR2RkJACurq5UVlZSV1eHWq1WODIhLNu1Zors\n2rWLRYsWoVarGTp0KE888QQACxYs4PDhw6hUKubNm9fYt4UQQgjRPJJICyGaRa1W4+joCMDq1asZ\nOnSoJNFCKKypmSKvvfYay5Ytw9fXl2nTpjF69GgKCwtJT08nLi6OtLQ05s2bR1xcnIKtEEIIIUyP\nSRUby8zMVDoERUi7LYuxt/uXX35h9erVvPzyyy1+L2Nvq6FYYrstsc1g+Hb369eP9957D7h0psjF\nY7u5ueHv74+VlRXDhg0jISGBhIQERo0aBUBYWBglJSWUlZW1OBb5jC2LtNv8WVJb/8wS222JbYaW\nt9ukRqR1Op3SIShC2m1ZjLnd27dv58MPP+Tjjz/GxcWlxe9nzG01JEtstyW2GQzf7mvNFMnLy0Oj\n0TQ+V6PRkJmZSVFREREREZfcn5eXh7Oz81WPk5mZ2ay2aLXaG22KSZN2W5am2h0aGtpGkRiW/N22\nHJbYZmh5u00qkRZCKKe0tJS3336bTz75BHd3d6XDEUL8ycWZIsuXL7/u1+r1+iafExQU1ORztFqt\n2SQQ10PabVkstd1CiMtJIi2EaJYNGzZQVFTE008/3XjfW2+9RUBAgIJRCSGuNlPEx8eH/Pz8xts5\nOTn4+PhgY2Nzyf25ubl4e3u3acxCCCGEqZNEWgjRLJMmTWLSpElKhyGE+JNrzRQJDAykrKyMrKws\n/Pz8iI+PZ+HChRQVFbF48WImT55McnIyPj4+15zWLYQQQojLSSItjI5er2fDztOoakuR2VNCXJmu\ntp4L5dWUlNVQXFbNhbJqistquFBeTX29nsGRAXQKckelUikdqjCgK80UGTBgAF26dCE2NpZXXnmF\nOXPmADB27FhCQkIICQkhIiKCyZMno1KpmD9/vlLhCwNIyyrmUEoetw8LQ602qZqywogkHD3L+fMl\nch4mxDW0KJFOSUnh8ccf57777mPatGmXPDZy5Ej8/Pwai54sXLgQX1/flhxOWIivN6fw5cYTAJTV\nOjDxpk6SDAiLpNfr2X4omyOp+ZSUNSTNJWXVlJTXUF557QIZ38an0sHfldgBwYzoE4SLo20bRS3a\nUlMzRfr163fFra2ee+45Q4YlFKLX61m86hBpWSWUlNfwwPiIpl8kxBV8G5/KqYwiunZuT3h7TdMv\nEMIC3XAiXVFRwauvvsqgQYOu+pylS5fi5OR0o4cQFih+fyZfbjyBj4cDOl0tn/10nIzzpcye1As7\nG9mzWFiOyupa/rv6MFsPZDXeZ6UCV2c7vN0dCGvnhruzHa7Otrg72+HmbIebsy1uznaUVer4dW8G\ne5LOs/S7JD5Zf4xBPfy5uX97enT0wspKLkwJYY5OZhSRllUCwNqtqYQGuDK8T9OF4oT4q/vGdWPe\nf3ey6IsDvDdnOA52MolViL+64V5ha2vL0qVLWbp0aWvGIyxYUlo+78cdwsnemlceHkRB3lm+2JLD\nbwezOF9Qzkv398fD1b7Fx6mtq+eH7Vo2J6YT3l7D+JhQQgLcWqEFQrSOjPMXeHPlXjJzyugS7MHM\nv0Xi4+GIs4NNs5Pg/t38KC6tJn5/Jpv2pLPtYDbbDmbjq3Ektn8wN/ULxsvdwcAtEUK0pR93nAZg\n5p2RrNxwjMXfHCLQx4WOQbLTgrg+3cO8GNnbi18P5rNsXRKzJvZSOiQhjM4NJ9LW1tZYW1/75fPn\nzyc7O5s+ffowZ86ca07PlT0qr83c251bXM27q9Oo19dz3+j26MrzcHW04aExAcRt1bP3ZBFPvrOF\nh8e2J9D7xk/+z5yvIG5rNmcLqlCpIDOnjM2JGXRs58SwSE+6d3A1itE6S9mjUlxu6/5Mlqw+THVN\nHRNiQrnv1ghsrG9snaO7ix13DO/I7cPCOHGmiE170tl+OJvPfz7BlxtPEBXuy80DgunXzQ9rWUsp\nhEkrKq1ix+FsAn2cGTu4Az4eDry6fA+vr9jDomeG4eHS8gvRwrKMHeCLNqeGjbvT6d/Nj/4RfkqH\nJIRRMdg8jSeffJKYmBjc3Nx44okn2LhxI2PGjLnq82WPyqsz93aXlFXzZtx2KqrreGpSL0b1bw80\ntLtL5478s1MY38ansnLDMd7/7jTPTIkiOvL6tlwqq6jh0w3H2bj7DHo9xPYPZsa4bpzMKOKHbVoO\nncojNbscH40jt0aHEDugPc4ONoZobpPM/fMWV1ajq2Pp90n8nHAGBztr5t7bj+ierbO1mEqlomuI\nhq4hGh6+vTvbD2WzaU86+47nsO94Du7OdtwaE8KEmDCZvieEidq0O53aOj23RoegUqno182PaWO6\n8tlPx3nz0728NjP6hi/KCctkrbZiztQ+PPOf31j8zSEWPzcCdxc7pcMSwmioX3nllVda8gaJiYk4\nODgQGRl5yf3h4eE4OjpiZWXFhQsXyMrKYsCAAS05FEVFRXh4eLToPUyRObe7RlfH//t4N6fPXmDi\nTZ24Y3inxscutlulUtEtxJPQADcSjp5j64Es1FYqIkI9myxCptfr2Xogi1eX7yFZW0Cwnwt/n9Gf\n8TGh2Nta087bmZF9g4juGUBdvZ4TZ4rYdzyHH3doKSipxM/TCTfntv3SMOfP+68sqa1/9td2ny8o\nZ/7SBPYey6GDvyuvzxxMRKinQY5tY62mY6A7owd2YHBkADZqK9KyS9h3PJdNe9JRq60IDXBr9Wq/\n8lmbP0tq658ZQ7vr6up558sDADwzJQob64aaIt1CNGTklHLgRC4Xymvo1631RhSNod1KsKR2FxUV\nERLsh52tNQlHz5GdW8bQ3u3MvgCsJX3GF1lim6Hl7TbIpcnS0lIefPBBampqANi7dy+dOnVq4lXC\n0uj1et6LO8jxM4XE9GrHtDFdr/n8Ad39eXt2DN4eDnz+8wkWfrGfal3dVZ+flVvKPz7cxaIvD1BZ\nXcd947rx3rPDr5igtPdzZdbEXnzy8s3cN64bzo62bNh1hsff3sL8jxLYdzyH+np9i9ssxF/tTjrH\n04u2kpZVQmz/YBY+NZQA77bZ07eDvysP396DZf+IZerNXajR1fPx90k8+sYv/Jxwhtq6+jaJQwjR\nMruTz1NQUsXIvkE42v8xm0qlUvH0pN508Hflp4Qz/JxwRqkQhQmbEBNKZEcvEo+dZ9OedKXDEcJo\n3PAcvqSkJN566y2ys7OxtrZm48aNjBw5ksDAQGJjYxk6dCiTJk3Czs6Obt26XXNat7BMX2w8wbaD\n2XTtoOHpyb2btTY5JMCNRU8NY8EniWw7mM25/IYiZJ5uf6ybrtbVserXFL7dkkptXT39uvny6B2R\n+Gocm3x/F0db/jayE7cPC2N38nl+2K7lwMlcDpzMpZ23Ew/d1oO+XWUbN9FytXX1rNxwnLVbU7G1\nUfPUpN6M6h+sSCyO9jZMGR3OuCGhfLvlFOt3aPlg9WHWxKcydXQXYnoHojaC2gFCiCu7WGRsXHTI\nZY/Z21nz0v39efY/2/jf2iME+7nQLcQwM16EebKyUvH05ChmvxPPx98n0aOjFwFebXPBVwhjdsOJ\ndPfu3fnss8+u+viMGTOYMWPGjb69MHNb9mUQtzkFP09HXrq/P7bXsbWVu4sdrz82mCWrDrNlXyZz\n3tvGPx4YQMdAdw6czOXDb49wrqAcLzd7HrmjBwO7+1/3NCS12oroyACiIwNIyyrmhx1afjuQzTtf\n7GfZP2IvueIvxPUqLtPxv//bybHThbTzdmLujP508HdVOixcnWy5f3wEE4aG8s0vKWzak847Xx5g\n9ZZT3DOmKwO7+5n9lD4hTE36uQscTcunZycvgnxdrvgcP08nXry3Ly9/lMAbn+7l3aeHSdV+cV28\nPRx47M5IFn6xn0VfHuCtJ4a0+hIgIUyN9ADR5o6m5rP4m0M4O9gw/6GBN7QG2cZazdOTe3P/rd0o\nvFDFi0t28MrSBOZ/lEBOUQW3DwvjgxdGMqhHQItP/MMC3Xl6chSTYztTVqnjp11nWvR+wrIdSsnl\n39+c4tjpQob0DGDR08OMIon+M083Bx77W08+nDuKm/oFkZlTyoJPEpnz3jYOnsxFr5dlDkIYix93\nXhyNvnaRyp6dvHlwfATFpdW8/kniNZdGCXElw6ICGdq7HSfTi1i15ZTS4QihOEmkRZvKzCnl9U8S\nAZh3X38Cfa589bw5VCoVd47oxD/uH4DaCvafyKVLsAfvPj2MByd0b/VR43FDQnG0t+a739Koqqlt\n1fcW5k2v13MkNY//9/Fu/vm/BCqr63n0jh68ML2vUc9u8NU48vTkKJY8P5LongGcyizm5Y8SmPd/\nO0nNLFY6PCEsXnmljvj9mXh7ONC/W9PLjsbHhDKybxCpmcV8sOqQXBQT1+2xOyPxcrPnq00nScko\nUjocIRQlibRoMyVl1fxr2W7KK3XMvrsXPTp6tcr79o/w491nhvP3Gf14e3YMoe3cWuV9/8rZwYZb\nh4RSXFYtxTZEs9TW1bP1QBZPv/sbL/3fLvYdz6FrBw1P3RnKrUNCTWaadJCvC3Pv7cd/nhlG366+\nJKUV8I8Pd1JeqVM6NCEs2q/7MqiqqeOWQR2aNc1WpVLxxF096RzsTvz+LL7fpm2DKIU5cXa05ekp\nUdTX61n05X6qqmVgQVguSaRFm6jR1fHa8j2cL6hgcmwXRvZt3aJK7bydGRwZ0KyCZS0xISYUO1s1\na+NT0dXKtDhxZRVVOtZuTeXhBb/wzhf7OXO2hOjIAP79ZAxvz46hvW/The+MUVigO/MfGsjUm7tQ\nXlUrF5SEUFB9vZ4NO09jrbbi5gHtm/06Wxs18+7rj4eLHSt+SOJQSq4BoxTmqGcnb24bGkZ2Xjkr\n1icrHY4QipFEWhhcfb2e/3x9kBPpRQzrHcjU0V2UDumGuTnbccugDuSXVLFlX6bS4Qgjk1dUyfIf\nkrn/1U0s/yGZ0ooabh0Swv/+Poq5M/oR3l6jdIit4taYUOxt1azbliZbZAmhkEOn8sjOK2do73bX\nXWvE082Beff1x8rKirc/28f5gnIDRSnM1b1ju9Lez4UNu86w73iO0uEIoQhJpIXB/bTrNNsPZdMt\nRMNTk3uZzHTWq7l9WBg21las3nKKOkkiBJCWVcw7X+zn4QWbG7ezmn5LV1b882YevSMSP08npUNs\nVS6OtsQOaE9+SRXbD2UrHY4QFulaW141R3gHDY/9LZLSCh2vLd9DRZUs1RDNZ2ujZs49fbBWW/F+\n3EFKyqqVDkmINnfD218J0RxlFTV8sfEkjvbWzJ3RDxvr5m9zZaw83RyI7R/Mhl1n2HYomxF9gpQO\nSSgk/dwFPvruKEdS84GGtcR3Dg9jWFSgWfyuX8ttQ8P4cYeWtVtTGR4VaPIXyExZSkoKjz/+OPfd\ndx/Tpk1rvD8nJ4fnnnuu8XZmZiZz5sxBp9Px3nvvERzcsMRm8ODBPPbYY20et7hx5wvK2Xv8PJ2D\n3ekc7HHD73PzgPZos0v4cedpZi2M5+Hbut/QlpHCMoUEuDH9lnBWrD/GB6sP8/cZ/eR3R1gUSaSF\nQcX9kkJpRQ33jeuGh4u90uG0mr+N6MTG3el880sKw3oHGnxttjA+eUWV/PN/uygqraZnJy/uGN6R\nqC4+FnMS4atxJLpnO7YfyuZQSh69u/goHZJFqqio4NVXX2XQoEGXPebr68tnn30GQG1tLdOnT2fk\nyJFs3LiRsWPH8uKLL7Z1uKKV/JxwBr2+6S2vmuOh27rjaG/N2q2pLPhkL33CfXj0jkj8vcxrJo0w\njNuGdSTxWA4JR8/x694MRvVv/np9IUydTO0WBnM2r4z1O7T4aBwZH9PyL3tj4qNxZESfILJyy0g4\nek7pcEQbq6jS8a9luykqrebBCd15bWY0fcJ9LSaJvuiO4WEArN2aqnAklsvW1palS5fi43PtCxlr\n165l9OjRODlJcmTqqnV1bNqTjquTLUN6BrT4/azVVtw7thvvzxlBz05e7D+RyxP/3sKXG09QI3tN\niyaorVQ8OyUKR3trPvruKEWlVUqHJESbkRFpYTCf/HiM2jo999/aDVsb85vmetdNndiyL4O4X04y\nOFKmwlmKuno9//58P2fOXeCWwR24bah5XSS6Hp2CPOgR5sXBlDxOny0hJMAwW8+Jq7O2tsbauumv\n8lWrVrF8+fLG24mJiTz44IPU1tby4osv0q1bt2u+PjMzE52u6TW0Wq1lbqfUlu3efbyQ0godo6K8\nycps3cr598f6cTDEge92nuOrTSfZtPs0f4vxJ6KD6xWfL5/3lYWGWtb3go/GkbtGdmLlhuMcPpXP\n8KhApUMSok1IIi0M4mhqPglHz9G1g4boyJZfMTdG7bydGdKzHdsOZbPveA79uvkpHZJoA8vXJbHv\neA69O3vz6O09LP4Cyh3Dwzials/arak8O7WP0uGIKzh48CChoaE4OzsD0LNnTzQaDcOHD+fgwYO8\n+OKL/PDDD9d8j6CgpmtBaLVai0sgoG3brdfree+7DKxUMGVsL3w8Wn8rvbAwGDtMx1ebTrJuu5aP\nfkxnYHc/Hr6tBz6aP44nn7f4sx5hXgCcPFMoibSwGDK1W7S6+no9H69LAhrWXplzojFxVGcA4jan\noNfrFY5GGNqPO7Ss264lyNeFF+/th1otf0L7hPsS5OvCtoPZ5BdXKh2OuIKtW7desoY6LCyM4cOH\nA9C7d28KCwupq5MpvKbgZHoR2uwSBnT3N0gSfZGjvQ0PTujOe88OJyLUk91J53ns7S2s+jUFXa3s\nViEuFxbohrXaihPphUqHIozQ6bMl/JKYoXQYrU7OAkWr27IvE212CcP7BLaomqgp6ODvysDufpzM\nKOLIqXylwxEGtP9EDh99dxR3ZzvmPzQQJwcbpUMyClZWKu4YFkZdvZ512y1zmqexO3r0KOHh4Y23\nly5dyvr164GGit8ajQa12vyW35ijH3e2bMur69XB35U3Ho/mmSm9cbSzZuWG4zz5TjyHT+W1yfGF\n6bCxVhMW6MbpsxeoqqlVOhxhZJb/kMx7cQfJyi1VOpRWJYm0aFWV1bV89tMxbG3U3HvLtdf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hYSIrzwdjc2NFubEsTJyzXkl898szuxaU2Ykk/OVQCwY61ICidj3ZL5Vd4933xyroKBQR33boia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\nbt1juWOVs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p1/u4X4u7ZVeWVq2roG2LY6fM5djBEmZ0NqMEdOlXH6au28SqQnavI7XuXJ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\n1ISvr++YtzU2NuLnZ/4cQkNDzd6nrKxs2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zz7aBJenmZnl9XnWM/U8Nvtn3KuppF74oey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\ngcnQ4jwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "SGh2-0E6YysZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Benchmark model definitions\n",
        "We will compare the RNNs against these models. For the time being you can regard them as black boxes."
      ]
    },
    {
      "metadata": {
        "id": "vVNz_D8bd8lA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# this is how to create a Keras model from neural network layers\n",
        "def compile_keras_sequential_model(list_of_layers, msg):\n",
        "  \n",
        "    # a tf.keras.Sequential model is a sequence of layers\n",
        "    model = tf.keras.Sequential(list_of_layers)\n",
        "    \n",
        "    # keras does not have a pre-defined metric for Root Mean Square Error. Let's define one.\n",
        "    def rmse(y_true, y_pred): # Root Mean Squared Error\n",
        "      return tf.sqrt(tf.reduce_mean(tf.square(y_pred - y_true)))\n",
        "    \n",
        "    print('\\nModel ', msg)\n",
        "    \n",
        "    # to finalize the model, specify the loss, the optimizer and metrics\n",
        "    model.compile(\n",
        "       loss = 'mean_squared_error',\n",
        "       optimizer = 'rmsprop',\n",
        "       metrics = [rmse])\n",
        "    \n",
        "    # this prints a description of the model\n",
        "    model.summary()\n",
        "    \n",
        "    return model\n",
        "  \n",
        "#\n",
        "# three very simplistic \"models\" that require no training. Can you beat them ?\n",
        "#\n",
        "\n",
        "# SIMPLISTIC BENCHMARK MODEL 1\n",
        "predict_same_as_last_value = lambda x: x[:,-1] # shape of x is [BATCHSIZE,SEQLEN]\n",
        "# SIMPLISTIC BENCHMARK MODEL 2\n",
        "predict_trend_from_last_two_values = lambda x: x[:,-1] + (x[:,-1] - x[:,-2])\n",
        "# SIMPLISTIC BENCHMARK MODEL 3\n",
        "predict_random_value = lambda x: tf.random.uniform(tf.shape(x)[0:1], -2.0, 2.0)\n",
        "\n",
        "def model_layers_from_lambda(lambda_fn, input_shape, output_shape):\n",
        "  return [tf.keras.layers.Lambda(lambda_fn, input_shape=input_shape),\n",
        "          tf.keras.layers.Reshape(output_shape)]\n",
        "\n",
        "model_layers_RAND  = model_layers_from_lambda(predict_random_value,               input_shape=[SEQLEN,], output_shape=[1,])\n",
        "model_layers_LAST  = model_layers_from_lambda(predict_same_as_last_value,         input_shape=[SEQLEN,], output_shape=[1,])\n",
        "model_layers_LAST2 = model_layers_from_lambda(predict_trend_from_last_two_values, input_shape=[SEQLEN,], output_shape=[1,])\n",
        "\n",
        "#\n",
        "# three neural network models for comparison, in increasing order of complexity\n",
        "#\n",
        "\n",
        "l = tf.keras.layers  # syntax shortcut\n",
        "\n",
        "# BENCHMARK MODEL 4: linear model (RMSE: 0.215 after 10 epochs)\n",
        "model_layers_LINEAR = [l.Dense(1, input_shape=[SEQLEN,])] # output shape [BATCHSIZE, 1]\n",
        "\n",
        "# BENCHMARK MODEL 5: 2-layer dense model (RMSE: 0.197 after 10 epochs)\n",
        "model_layers_DNN = [l.Dense(SEQLEN//2, activation='relu', input_shape=[SEQLEN,]), # input  shape [BATCHSIZE, SEQLEN]\n",
        "                    l.Dense(1)] # output shape [BATCHSIZE, 1]\n",
        "\n",
        "# BENCHMARK MODEL 6: convolutional (RMSE: 0.186 after 10 epochs)\n",
        "model_layers_CNN = [\n",
        "    l.Reshape([SEQLEN, 1], input_shape=[SEQLEN,]), # [BATCHSIZE, SEQLEN, 1] is necessary for conv model\n",
        "    l.Conv1D(filters=8, kernel_size=4, activation='relu', padding=\"same\"), # [BATCHSIZE, SEQLEN, 8]\n",
        "    l.Conv1D(filters=16, kernel_size=3, activation='relu', padding=\"same\"), # [BATCHSIZE, SEQLEN, 8]\n",
        "    l.Conv1D(filters=8, kernel_size=1, activation='relu', padding=\"same\"), # [BATCHSIZE, SEQLEN, 8]\n",
        "    l.MaxPooling1D(pool_size=2, strides=2),  # [BATCHSIZE, SEQLEN//2, 8]\n",
        "    l.Conv1D(filters=8, kernel_size=3, activation='relu', padding=\"same\"),  # [BATCHSIZE, SEQLEN//2, 8]\n",
        "    l.MaxPooling1D(pool_size=2, strides=2),  # [BATCHSIZE, SEQLEN//4, 8]\n",
        "    # mis-using a conv layer as linear regression :-)\n",
        "    l.Conv1D(filters=1, kernel_size=SEQLEN//4, activation=None, padding=\"valid\"), # output shape [BATCHSIZE, 1, 1]\n",
        "    l.Reshape([1,]) ] # output shape [BATCHSIZE, 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "04lT11FL7FBL",
        "colab_type": "code",
        "outputId": "d53a620e-7aa3-4b00-f42b-48b73c7acefb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1695
        }
      },
      "cell_type": "code",
      "source": [
        "# instantiate the benchmark models\n",
        "model_RAND   = compile_keras_sequential_model(model_layers_RAND, \"RAND\")\n",
        "model_LAST   = compile_keras_sequential_model(model_layers_LAST, \"LAST\")\n",
        "model_LAST2  = compile_keras_sequential_model(model_layers_LAST2, \"LAST2\")\n",
        "model_LINEAR = compile_keras_sequential_model(model_layers_LINEAR, \"LINEAR\")\n",
        "model_DNN    = compile_keras_sequential_model(model_layers_DNN, \"DNN\")\n",
        "model_CNN    = compile_keras_sequential_model(model_layers_CNN, \"CNN\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model  RAND\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/control_flow_ops.py:423: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lambda (Lambda)              (None,)                   0         \n",
            "_________________________________________________________________\n",
            "reshape (Reshape)            (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 0\n",
            "Trainable params: 0\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "\n",
            "Model  LAST\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lambda_1 (Lambda)            (None,)                   0         \n",
            "_________________________________________________________________\n",
            "reshape_1 (Reshape)          (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 0\n",
            "Trainable params: 0\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "\n",
            "Model  LAST2\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lambda_2 (Lambda)            (None,)                   0         \n",
            "_________________________________________________________________\n",
            "reshape_2 (Reshape)          (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 0\n",
            "Trainable params: 0\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "\n",
            "Model  LINEAR\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 17\n",
            "Trainable params: 17\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "\n",
            "Model  DNN\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_1 (Dense)              (None, 8)                 136       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 145\n",
            "Trainable params: 145\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "\n",
            "Model  CNN\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_3 (Reshape)          (None, 16, 1)             0         \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 16, 8)             40        \n",
            "_________________________________________________________________\n",
            "conv1d_1 (Conv1D)            (None, 16, 16)            400       \n",
            "_________________________________________________________________\n",
            "conv1d_2 (Conv1D)            (None, 16, 8)             136       \n",
            "_________________________________________________________________\n",
            "max_pooling1d (MaxPooling1D) (None, 8, 8)              0         \n",
            "_________________________________________________________________\n",
            "conv1d_3 (Conv1D)            (None, 8, 8)              200       \n",
            "_________________________________________________________________\n",
            "max_pooling1d_1 (MaxPooling1 (None, 4, 8)              0         \n",
            "_________________________________________________________________\n",
            "conv1d_4 (Conv1D)            (None, 1, 1)              33        \n",
            "_________________________________________________________________\n",
            "reshape_4 (Reshape)          (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 809\n",
            "Trainable params: 809\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "idWk7K3FYysn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Prepare datasets"
      ]
    },
    {
      "metadata": {
        "id": "ydOmIaCtYyso",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# training to predict the same sequence shifted by one (next value)\n",
        "labeldata = np.roll(data, -1)\n",
        "\n",
        "# cut data into sequences\n",
        "traindata = np.reshape(data, [-1, SEQLEN])\n",
        "labeldata = np.reshape(labeldata, [-1, SEQLEN])\n",
        "\n",
        "# make an evaluation dataset by cutting the sequences differently\n",
        "evaldata = np.roll(data, -SEQLEN//2)\n",
        "evallabels = np.roll(evaldata, -1)\n",
        "evaldata = np.reshape(evaldata, [-1, SEQLEN])\n",
        "evallabels = np.reshape(evallabels, [-1, SEQLEN])\n",
        "\n",
        "def get_training_dataset(last_n=1):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(\n",
        "      (\n",
        "          traindata, # features\n",
        "          labeldata[:,-last_n:SEQLEN] # targets: the last element or last n elements in the shifted sequence\n",
        "      )\n",
        "  )\n",
        "  # Dataset API used here to put the dataset into shape\n",
        "  dataset = dataset.repeat()\n",
        "  dataset = dataset.shuffle(DATA_LEN//SEQLEN) # shuffling is important ! (Number of sequences in shuffle buffer: all of them)\n",
        "  dataset = dataset.batch(BATCHSIZE, drop_remainder = True)\n",
        "  return dataset\n",
        "\n",
        "def get_evaluation_dataset(last_n=1):\n",
        "  dataset = tf.data.Dataset.from_tensor_slices(\n",
        "      (\n",
        "          evaldata, # features       \n",
        "          evallabels[:,-last_n:SEQLEN] # targets: the last element or last n elements in the shifted sequence\n",
        "      )\n",
        "  )\n",
        "  # Dataset API used here to put the dataset into shape\n",
        "  dataset = dataset.batch(evaldata.shape[0], drop_remainder = True) # just one batch with everything\n",
        "  return dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Q443hxmJ5LNe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Peek at the data"
      ]
    },
    {
      "metadata": {
        "id": "FN027ePq5Kxy",
        "colab_type": "code",
        "outputId": "014732eb-6292-4382-c0c5-43b282db16f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "cell_type": "code",
      "source": [
        "train_ds = get_training_dataset()\n",
        "for features, labels in train_ds.take(10):\n",
        "  print(\"input_shape:\", features.numpy().shape, \", shape of labels:\", labels.numpy().shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n",
            "input_shape: (32, 16) , shape of labels: (32, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CuNpxRbP_QYQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## RNN models"
      ]
    },
    {
      "metadata": {
        "id": "XEyJklu-Yysi",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![deep RNN schematic](https://googlecloudplatform.github.io/tensorflow-without-a-phd/images/RNN1.svg)\n",
        "<div style=\"text-align: right; font-family: monospace\">\n",
        "  X shape [BATCHSIZE, SEQLEN, 1]<br/>\n",
        "  Y shape [BATCHSIZE, SEQLEN, 1]<br/>\n",
        "  H shape [BATCHSIZE, RNN_CELLSIZE*N_LAYERS]\n",
        "</div>"
      ]
    },
    {
      "metadata": {
        "id": "zPsQiS8pYysi",
        "colab_type": "code",
        "outputId": "e187785a-e403-443e-eb89-4385bda7d0d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "cell_type": "code",
      "source": [
        "# RNN model (RMSE: 0.164 after 10 epochs)\n",
        "model_layers_RNN = [\n",
        "    l.Reshape([SEQLEN, 1], input_shape=[SEQLEN,]), # [BATCHSIZE, SEQLEN, 1] is necessary for RNN model\n",
        "    l.GRU(RNN_CELLSIZE, return_sequences=True),  # output shape [BATCHSIZE, SEQLEN, RNN_CELLSIZE]\n",
        "    l.GRU(RNN_CELLSIZE), # keep only last output in sequence: output shape [BATCHSIZE, RNN_CELLSIZE]\n",
        "    l.Dense(1) # output shape [BATCHSIZE, 1]\n",
        "]\n",
        "\n",
        "model_RNN = compile_keras_sequential_model(model_layers_RNN, \"RNN\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model  RNN\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_5 (Reshape)          (None, 16, 1)             0         \n",
            "_________________________________________________________________\n",
            "gru (GRU)                    (None, 16, 32)            3264      \n",
            "_________________________________________________________________\n",
            "gru_1 (GRU)                  (None, 32)                6240      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 9,537\n",
            "Trainable params: 9,537\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "D5Kp1zjx4dY1",
        "colab_type": "code",
        "outputId": "32c3dea7-1dce-4b76-b259-5abcdef1b1a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "cell_type": "code",
      "source": [
        "# RNN model with loss computed on last N elements (RMSE: 0.163 after 10 epochs)\n",
        "model_layers_RNN_N = [\n",
        "    l.Reshape([SEQLEN, 1], input_shape=[SEQLEN,]), # [BATCHSIZE, SEQLEN, 1] is necessary for RNN model\n",
        "    l.GRU(RNN_CELLSIZE, return_sequences=True),\n",
        "    l.GRU(RNN_CELLSIZE, return_sequences=True), # output shape [BATCHSIZE, SEQLEN, RNN_CELLSIZE]\n",
        "    l.TimeDistributed(l.Dense(1)),              # output shape [BATCHSIZE, SEQLEN, 1]\n",
        "    l.Lambda(lambda x: x[:,-LAST_N:SEQLEN,0]) # last N item(s) in sequence: output shape [BATCHSIZE, LAST_N]\n",
        "]\n",
        "\n",
        "model_RNN_N = compile_keras_sequential_model(model_layers_RNN_N, 'RNN_N')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Model  RNN_N\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape_6 (Reshape)          (None, 16, 1)             0         \n",
            "_________________________________________________________________\n",
            "gru_2 (GRU)                  (None, 16, 32)            3264      \n",
            "_________________________________________________________________\n",
            "gru_3 (GRU)                  (None, 16, 32)            6240      \n",
            "_________________________________________________________________\n",
            "time_distributed (TimeDistri (None, 16, 1)             33        \n",
            "_________________________________________________________________\n",
            "lambda_3 (Lambda)            (None, 8)                 0         \n",
            "=================================================================\n",
            "Total params: 9,537\n",
            "Trainable params: 9,537\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xKoWDXEC_InP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Training loop"
      ]
    },
    {
      "metadata": {
        "id": "KtqXxnUHhTHi",
        "colab_type": "code",
        "outputId": "76a53d4e-47e2-4251-bead-dafdd3a9555a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        }
      },
      "cell_type": "code",
      "source": [
        "# You can re-execute this cell to continue training\n",
        "\n",
        "NB_EPOCHS = 3      # number of times the data is repeated during training\n",
        "steps_per_epoch = DATA_LEN // SEQLEN // BATCHSIZE\n",
        "\n",
        "model = model_RNN_N # model to train: model_LINEAR, model_DNN, model_CNN, model_RNN, model_RNN_N\n",
        "train_ds = get_training_dataset(last_n=LAST_N) # use last_n=LAST_N for model_RNN_N\n",
        "\n",
        "history = model.fit(train_ds, steps_per_epoch=steps_per_epoch, epochs=NB_EPOCHS)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "1024/1024 [==============================] - 23s 22ms/step - loss: 0.0694 - rmse: 0.2428\n",
            "Epoch 2/3\n",
            "1024/1024 [==============================] - 20s 19ms/step - loss: 0.0394 - rmse: 0.1982\n",
            "Epoch 3/3\n",
            "1024/1024 [==============================] - 19s 19ms/step - loss: 0.0375 - rmse: 0.1935\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "br7nVK9ijnCU",
        "colab_type": "code",
        "outputId": "64b08b73-ea03-4d44-c4ad-b92ace44c178",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2ebg10F/+9Z1mvrRNGScuGB0NAAAAqFUUbADVon/XZoqbHa5f3d5Gx05dVPTq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\n2ICADQAAAACADf4fIm/CLQNyciYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "N9RkMeEXW_qQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Evaluation"
      ]
    },
    {
      "metadata": {
        "id": "GyOzPv8Znu61",
        "colab_type": "code",
        "outputId": "e53330a2-ec94-46b2-da0b-28c4b4770191",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        }
      },
      "cell_type": "code",
      "source": [
        "# Here \"evaluating\" using the training dataset\n",
        "eval_ds = get_evaluation_dataset(last_n=LAST_N) # use last_n=LAST_N for model_RNN_N\n",
        "loss, rmse = model.evaluate(eval_ds, steps=1)\n",
        "\n",
        "# compare agains simplistic benchmark models that require no training\n",
        "ds = get_evaluation_dataset()\n",
        "_, rmse1 = model_RAND.evaluate(get_evaluation_dataset(), steps=1, verbose=0)\n",
        "_, rmse2 = model_LAST.evaluate(get_evaluation_dataset(), steps=1, verbose=0)\n",
        "_, rmse3 = model_LAST2.evaluate(get_evaluation_dataset(), steps=1, verbose=0)\n",
        "\n",
        "picture_this_hist(rmse1, rmse2, rmse3, rmse)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r1/1 [==============================] - 2s 2s/step - loss: 0.0380 - rmse: 0.1949\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vaYqKiuLEiRNoNBrmzJlDt27dlLbvv/+eZcuWYWVlRYcOHYiMjMTKSvJTCFF3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\ncL+hEQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "qJeFuhNRXCmq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Predictions"
      ]
    },
    {
      "metadata": {
        "id": "YpewCYd2Yys3",
        "colab_type": "code",
        "outputId": "7f1a4bc1-d959-414b-eb7b-f075e6440f09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "cell_type": "code",
      "source": [
        "# execute multiple times to see different sample sequences\n",
        "subset = np.random.choice(DATA_LEN//SEQLEN, 8) # pick 8 eval sequences at random\n",
        "\n",
        "predictions = model.predict(evaldata[subset], steps=1) # prediction directly from numpy array\n",
        "picture_this_3(predictions[:,-1], evaldata[subset], evallabels[subset], SEQLEN)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I5TJSssukDkXoRmm55az86RxJGaXYWqn408wwxg/xvuPzAYVCzoiQHuw9mc6l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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "0ScB9vM_Yys6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "<a name=\"benchmark\"></a>\n",
        "## Benchmark\n",
        "Benchmark all the algorithms. This takes a while (approx. 10 min)."
      ]
    },
    {
      "metadata": {
        "id": "_EUH_ZsZKh1D",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# instantiate all models models again\n",
        "model_RAND   = compile_keras_sequential_model(model_layers_RAND, \"RAND\")\n",
        "model_LAST   = compile_keras_sequential_model(model_layers_LAST, \"LAST\")\n",
        "model_LAST2  = compile_keras_sequential_model(model_layers_LAST2, \"LAST2\")\n",
        "model_LINEAR = compile_keras_sequential_model(model_layers_LINEAR, \"LINEAR\")\n",
        "model_DNN    = compile_keras_sequential_model(model_layers_DNN, \"DNN\")\n",
        "model_CNN    = compile_keras_sequential_model(model_layers_CNN, \"CNN\")\n",
        "model_RNN    = compile_keras_sequential_model(model_layers_RNN, \"RNN\")\n",
        "model_RNN_N  = compile_keras_sequential_model(model_layers_RNN_N, 'RNN_N')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pzUW6uMtYys6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "NB_EPOCHS = 10\n",
        "\n",
        "# train all models\n",
        "for i, model in enumerate([model_LINEAR, model_DNN, model_CNN, model_RNN]):\n",
        "  print(\"Trainig \", [\"model_LINEAR\", \"model_DNN\", \"model_CNN\", \"model_RNN\"][i])\n",
        "  model.fit(get_training_dataset(), steps_per_epoch=steps_per_epoch, epochs=NB_EPOCHS)\n",
        "\n",
        "print(\"Training \", \"model_RNN_N\")\n",
        "model_RNN_N.fit(get_training_dataset(last_n=LAST_N), steps_per_epoch=steps_per_epoch, epochs=NB_EPOCHS)\n",
        "\n",
        "# evaluate all models\n",
        "rmses = []\n",
        "for model in [model_RAND, model_LAST, model_LAST2, model_LINEAR, model_DNN, model_CNN, model_RNN]:\n",
        "  _, rmse = model.evaluate(get_evaluation_dataset(), steps=1)\n",
        "  rmses.append(rmse)\n",
        "\n",
        "_, rmse = model_RNN_N.evaluate(get_evaluation_dataset(last_n=LAST_N), steps=1)\n",
        "rmses.append(rmse)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gZt1F7yjgdAQ",
        "colab_type": "code",
        "outputId": "9b83eb92-6cad-470b-d0c5-1ff8d7c33d8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        }
      },
      "cell_type": "code",
      "source": [
        "print(rmses)\n",
        "picture_this_hist_all(*rmses)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.5435231, 0.41145748, 0.33609793, 0.21479364, 0.196844, 0.1857681, 0.16368383, 0.16305003]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5HomYW8TMgJi5RcpscNjQx8cHSUlJAIDMzExoNBp5qBAA3nnnHTz77LPw9fWVH9u+fTvW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\neRERkXD+H6gPmtFOV5PLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 504x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "2VgeUgL-Yys8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Copyright 2019 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "[http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0)\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    }
  ]
}